DE102024107607A1 - OBJECT DETECTION METHOD AND OPTICAL DETECTION SYSTEM - Google Patents

Abstract

The present disclosure relates to a method for object detection using first and second optical data (21, 22) of a detection area (32), wherein the first optical data (21) comprise active image data and the second optical data (22) comprise passive image data, the method comprising:
Receiving the first and second optical data (21, 22) from an optical receiving sensor (12), wherein the optical receiving sensor (12) comprises a plurality of pixels, wherein the pixels are designed to provide the first and second optical data (21, 22) of the detection area (32),
Performing an object detection in the detection area (32) using the first and second optical data (21, 22).
The present disclosure further relates to an optical detection system (10) and a vehicle (30) comprising the optical detection system (10).

Description

Technical area

The present disclosure relates to a method for object detection using first and second optical data, an optical detection system, and a vehicle including such an optical detection system.

background

Modern vehicles such as cars, vans, trucks, motorcycles, etc. may include sensor systems whose data is used for driver information and/or used by driver assistance systems.

Sensor systems are continually being developed for various functions, e.g., for detecting environmental information in the near and far range of vehicles, such as passenger cars or commercial vehicles. Based on the acquired data, a model of the vehicle's environment can be created, and a reaction to changes in this environment is possible. Sensor systems can also serve as sensors for driver assistance systems, particularly assistance systems for autonomous or semi-autonomous vehicle control. They can be used, for example, to detect obstacles and/or other road users in front of, behind, or in the vehicle's blind spot. Sensor systems can be based on different sensor principles, such as radar, ultrasound, or optics.

Optical detection systems include passive optical detection systems such as a camera and active optical detection systems such as a LIDAR (Light Detection and Ranging) system. A LIDAR system comprises an optical transmission device and an optical reception device. The transmission device emits an optical signal, which can be continuous or pulsed. Furthermore, the optical signal can be modulated. For example, electromagnetic waves in the form of laser beams in the ultraviolet, visible, or infrared range can be used as optical signals in a LIDAR system. The light is received by the optical reception device after being reflected from an object in a detection zone of the LIDAR system. The optical signal can be evaluated, for example, using a time-of-flight method, and the spatial position and distance of the object at which the reflection occurred can be determined. It may also be possible to determine a relative velocity. Reflection or reflected light is understood herein to mean any light that is reflected back and is intended to include, in particular, light that is reflected back by scattering or absorption emission.

An optical receiving sensor of an optical detection system may comprise multiple receiving elements, called pixels. A pixel of the optical receiving sensor generates an electrical signal in response to the reception of an optical signal. The pixels may be configured to receive light from different reception angles.

In US2016/0054434 A dual-mode imaging device is described. The imaging device comprises a pixelated array of semiconductor detector elements, each detection element electrically connected to an integrated circuit, the integrated circuit of each of the pixels comprising a passive signal path and a transient signal path. The passive path provides imaging of successive frames or scenes, and the transient path detects transient electromagnetic events, such as laser pulses. The passive path and the transient path operate simultaneously.

In US2019/0353791A1 An electronic device is described that is designed to receive first image data from a passive optical sensor and second data from an active sensor. The data from the active sensor is used to supplement the image data from the passive optical sensor.

Brief description

• • Empfangen der ersten und zweiten optischen Daten von einem optischen Empfangssensor, wobei der optische Empfangssensor eine Vielzahl von Pixeln umfasst, wobei die Pixel dazu ausgelegt sind, die ersten und zweiten optischen Daten des Detektionsbereichs bereitzustellen.
• • Durchführen einer Objektdetektion im Detektionsbereich unter Verwendung der ersten und zweiten optischen Daten.

A method for object detection uses first and second optical data of a detection area. The first optical data comprises active image data, and the second optical data comprises passive image data. The method comprises:

• • Receiving the first and second optical data from an optical receiving sensor, wherein the optical receiving sensor comprises a plurality of pixels, the pixels being configured to provide the first and second optical data of the detection area.
• • Performing object detection in the detection area using the first and second optical data.

The use of the first and second optical data to perform the object detection enables self-validation of the object detection. The use of the first and second optical data, which are generated by the same optical receiver The capture sensor enables object detection, including self-validation, to be performed in a very short time frame, for example in real time, e.g., within nanoseconds.

The optical receiving sensor comprises pixels, which contain light-sensitive devices that capture light and convert it into an electrical signal that can be further processed, e.g., measured, analyzed, and interpreted. The light-sensitive devices can include photodiodes, charge-coupled devices (CCDs), CMOS sensors, photomultiplier tubes, single-photon avalanche diodes (SPADs), or similar.

The optical receiving sensor provides both the first and second optical data. It is designed to provide both the active image data and the passive image data of the detection area. Each pixel is designed to output the active image data and the passive image data. The detection area is an area in the vicinity of the optical receiving sensor from which the optical signals are detected.

The optical receiving sensor can be included in an optical detection system. The optical detection system uses optical signals to detect and/or measure objects and/or conditions within its detection range. The detection range is an area in the vicinity of the optical detection system that is covered by the optical detection system. The optical detection system is a combined active and passive optical detection system, wherein the optical receiving sensor included in the optical detection system is configured to detect both first and second optical signals and output active and passive image data using the first and second optical signals.

Active image data is obtained using optical signals emitted by an optical source of the optical detection system. Such emitted optical signals are generated, for example, by lasers or LEDs contained in the optical source. The emitted optical signal is then reflected back into the detection area and analyzed. Active image data is obtained by actively illuminating the detection area, capturing, and analyzing the reflected light. Examples of active optical detection systems include lidar systems.

Passive image data is obtained based on ambient light or heat emissions from objects in the detection zone, for example. Passive image data is obtained by capturing and analyzing light or radiation that naturally radiates from or is reflected within the detection zone.

In some embodiments, the first optical data comprising the active image data may be acquired when the reflected light is expected to return from an illumination period. The first optical data may, in particular, comprise 3D image data.

3D image data is sometimes referred to as point cloud data. The points in the point cloud contain information about the spatial location and depth of the reflection points in the environment. The reflection points are the points at which the reflection of the optical signal emitted and received by the optical detection system occurred. The point cloud can be understood as a set of points, where each point contains corresponding coordinates in a specific three-dimensional coordinate system. In the case of a three-dimensional point cloud, the three-dimensional coordinates can be determined, for example, by the direction of incidence of a light beam reflected at a reflection point and the corresponding propagation time or radial distance, also known as depth, measured for that particular point. The direction of incidence can be given, for example, by the horizontal angle and the elevation angle. In addition to the spatial information, e.g., the three-dimensional coordinates that include the distance or depth to the point, the point cloud can also store additional information or measurement data for the individual points, such as the pulse width of the respective received optical signal.

The three-dimensional coordinate system of the point cloud can be a three-dimensional polar coordinate system. However, the information can also be given in Cartesian coordinates for each of the points. Conversion between the coordinate systems is possible. In addition to the 3D image data, the point cloud can also include 2D image data.

The second optical data comprising the passive image data may be acquired by collecting light over periods of time and/or during intervals that differ from the intervals at which active image data is acquired. The second optical data may comprise 2D image data, for example, grayscale image data.

A grayscale image, sometimes called a gray-value image, is a type of digital image that contains only shades of gray, ranging from black at its weakest intensity to white at its strongest. Unlike color images, which are represented using multiple channels, such as red, green, and blue, grayscale images have a single channel, with each pixel representing the intensity of light at that point, with no color information. In the context of digital imaging, each pixel in a grayscale image has a value that represents the brightness of that pixel. Using grayscale image data in image processing has the advantage of reducing the amount of data to be processed, thus simplifying the required processing.

The point cloud may include the first optical data, which comprises the active image data, and the second optical data, which comprises the passive image data. The point cloud may be generated by the optical detection system. The optical detection system may be contained in a vehicle. The point cloud data may be further processed in the vehicle, e.g., by a computing unit. Such further processing may include, for example, further processing of the point cloud and/or its use by functions for autonomous or semi-autonomous driving.

Object detection involves identifying and locating objects in the first and second optical data. The objects, along with the second optical data, can be output in a graphical manner that visualizes the location, shape, etc., of the object in the passive image of the surroundings of the detection area. Object identification may include a feature extraction step. In this step, relevant features that can assist in identifying objects are extracted from the optical data. Such extracted features may include local geometric features such as normals and/or curvatures.

In one embodiment of the method, the detected objects can be output as a list—optionally together with the second optical data. The list of detected objects can further be used by other algorithms to perform tasks such as functions for autonomous or semi-autonomous driving.

In one embodiment of the method, object detection further comprises performing clustering, edge detection, and/or segmentation on the first optical data and separately on the second optical data. The segmentation and/or clustering of the optical data into clusters and/or segments, potentially representing individual objects or parts of objects, is performed separately on the first and second optical data. The output of the respective separate steps can then be used to check and/or enhance and/or correct the output of the respective other step.

In one embodiment of the method, object detection using the first optical data comprises a classification step using artificial intelligence. The classification may include a feature extraction step in which significant and informative attributes of the first optical data are identified and extracted. These features could relate to the shape, texture, or geometric properties of objects in the first optical data. In some approaches, deep learning models can be used to automatically learn and extract these features directly from the data, without the need for manual feature design. Once the features are extracted, a classification model, e.g., a neural network, is trained in the next step. Once trained, the model can perform the classification.

In one embodiment of the method, object detection using the second optical data comprises image signal processing. Image signal processing (ISP) may involve a series of steps to transform the second optical data acquired by the optical receiving sensor. Examples may include filtering, demosaicing, interpolation, color correction, noise reduction, sharpening, lens distortion correction, chromatic aberration correction, color space correction, and/or compression. These steps may be implemented in various orders and combinations.

In embodiments of the method, object detection using the first optical data comprises generating at least one list of candidate objects using the first optical data. The output of the object detection step or algorithm can thus be a list of candidate objects identified in the first optical data, which includes active image data.

In embodiments of the method, the at least one list of candidate objects comprises a list of dynamic candidate objects and a list of static candidate objects. There may be a list of static candidate objects that are generated using of the first optical data, and there may be a list of dynamic candidate objects generated using the first optical data. Dynamic objects are objects that move. The object detection used to detect the objects in the first optical data may additionally be capable of detecting whether the object is static or dynamic.

In embodiments of the method, object detection using the second optical data comprises at least one further list of candidate objects using the second optical data. The output of the object detection step or algorithm can thus be a further list of candidate objects identified in the second optical data, which comprises passive image data.

In embodiments of the method, the at least one further list of candidate objects comprises a further list of dynamic candidate objects and a further list of static candidate objects. There may be a further list of static candidate objects generated using the second optical data, and there may be a further list of dynamic candidate objects generated using the second optical data. The object detection used to detect the objects in the second optical data may additionally be capable of detecting whether the object is static or dynamic.

The list of objects can be generated from the at least one list of candidate objects and the at least one further list of candidate objects. The list of objects includes the objects identified by the described object detection method. The list of objects can then be output.

In one embodiment of the method, a confidence measure is calculated using the at least one list of candidate objects and the at least one further list of candidate objects. The confidence measure is then output together with the object list. The use of the object list in further processing steps can thus be controlled by the confidence measure. If the confidence measure is too low, this can be taken into account, for example, for the further processing steps, especially if safety is at stake. For example, the confidence measure can influence trajectory planning and/or emergency braking handling by a driving function in the vehicle.

An optical detection system comprises the optical receiving sensor. The optical receiving sensor comprises a plurality of pixels, wherein the pixels are configured to provide the first and second optical data of the detection area. The first optical data comprises active image data, and the second optical data comprises passive image data. The optical detection system further comprises a processor configured to perform object detection in the detection area using the first and second optical data.

Using the first and second optical data provided by the same optical receiving sensors enables the quality of object detection of the optical detection system to be improved. The first and second optical data refer to the same detection area covered by the optical receiving sensor. The processing speed can be fast because the processing can be performed when the first and second optical data are generated by the optical receiving sensors.

The optical detection system may further comprise an optical source used to obtain the active image data, wherein the optical receiving sensor and the optical source are contained in a real-time environment. The real-time environment is configured to enable real-time processing within tight time constraints, as required by real-time applications. By placing the optical receiving sensor and the optical source in the same real-time processing environment, object detection can be performed at such high speed that it can be applied to functions with tight time constraints and the need for highly accurate and timely data.

In embodiments of the optical detection system, the optical receiving sensor and the optical source are arranged on a single chip. This can further ensure fast processing under tight time constraints.

In one embodiment of the optical detection system, the processor is designed to perform the steps of the described method for object detection.

The optical detection system can be incorporated into a vehicle. The output of the optical detection system, e.g., the generated optical data and/or the list of detected objects, can be further processed, for example, by a computing unit in the vehicle.

Short description of the characters

• 1 veranschaulicht schematisch ein Verfahren zur Objektdetektion unter Verwendung erster und zweiter optischer Daten.
• 2 veranschaulicht schematisch ein Fahrzeug mit einem optischen Detektionssystem.
• 3 veranschaulicht schematisch Listen von Kandidatenobjekten.
• 4 veranschaulicht schematisch weitere Listen von Kandidatenobjekten.
• 5 veranschaulicht schematisch eine Liste von Objekten.
• 6 veranschaulicht schematisch ein optisches Detektionssystem.

Embodiments will now be described, by way of example only, with reference to the attached drawing figures. Similar reference numerals are used throughout to refer to similar elements. The illustrated structures and devices are not necessarily drawn to scale.

• 1 schematically illustrates a method for object detection using first and second optical data.
• 2 schematically illustrates a vehicle with an optical detection system.
• 3 schematically illustrates lists of candidate objects.
• 4 schematically illustrates further lists of candidate objects.
• 5 schematically illustrates a list of objects.
• 6 schematically illustrates an optical detection system.

Detailed description

1 schematically illustrates a method for object detection using first optical data 21 and second optical data 22 of a detection area 32. The first optical data 21 comprise active image data, and the second optical data 22 comprise passive image data.

In 100, the first and second optical data 21, 22 are received by an optical receiving sensor 12. The optical receiving sensor 12 comprises a plurality of pixels, wherein the pixels are configured to provide the first and second optical data 21, 22 of the detection area (32).

In 102, the object detection in the detection area 32 is carried out using the first optical data 21.

In 104, the object detection in the detection area 32 is carried out using the second optical data 22.

In 106 the information obtained in 102 and 104 is merged.

In 108 the information obtained in 106 is validated.

In 110 the information received in 108 is output.

2 schematically illustrates a vehicle 30, for example, a passenger car. The vehicle 30 includes an optical detection system 10. The optical detection system 10 is arranged in a front region of the vehicle 30. It includes a processor 20 and a real-time environment 16, e.g., a real-time chip. The real-time environment 16 includes an optical source 14 and an optical receiving sensor 12. The optical source 14 is designed to transmit optical signals 36 into the detection region 32. After reflection in the detection region 36, e.g., from an object O, the optical signals 36 are received by the optical receiving sensor 12.

The optical detection system 10 may optionally include an optical deflection device that may be arranged to deflect the optical signal 36 transmitted by the optical source 14 and to be received by the optical receiving sensor 12. The optical deflection device may be controlled such that the optical signal L performs a scanning movement across the detection area 32. The optical deflection device may, for example, comprise a rotating mirror that performs a rotating movement to deflect the optical signal 36 such that the scanning movement is performed by the optical signal 36.

The optical detection system 10 is, for example, a lidar system that has the capability of detecting the second optical data comprising the passive image data 22 and the first optical data comprising the active image data 21 with a single optical receiving sensor 12 located in the real-time environment 16, e.g., on a single real-time chip.

The second optical data 22, which comprises the passive image data, is captured by the optical receiving sensor 12 by detecting ambient light, similar to a camera.

The first optical data 21, which comprises the active image data, is generated by the optical detection system 10 by emitting the optical signal 36 using the optical source 14 and detecting the optical signal 36 reflected in the detection area 32.

The processor 20 can control the transmission and reception process of the optical signal 36 and the reception of the second optical data 22 comprising the passive image data.

In the processor 20, the transmitted and received optical signals 36 can be evaluated, for example using run time measurements to obtain the first optical data 21. The first optical data 21 contains the collection of measurement data for the various reflection points of the optical signal 36 in the coverage area 32. For example, each light pulse of the optical signal 36 can provide one data point.

The second optical data 22 contains the collection of received passive image data of the detection area 32. This data can be used to enhance and verify the first optical data 21.

Object detection in the detection area 32 is then performed by the processor 20 using the first and second optical data 21, 22.

The data generated in this way can be transmitted from the processor 20 to a computing unit 34 of the vehicle 30.

The optical detection system 10 can, for example, be placed or integrated at the front of the vehicle 30. Accordingly, an area in the direction of travel in front of the vehicle 30 can be monitored by the optical detection system 10. Detection systems 10 can be placed on other parts of the vehicle 30, e.g., for all-round visibility functions, such as the sides and/or rear of the vehicle 30. It is also possible to arrange detection systems and/or other sensors such as radar, ultrasound, etc. on the vehicle 30, even in corner areas of the vehicle 30. Sensor fusion can then be performed using the sensor data from the various sensors.

The optical detection system 10 can be used to detect features of the environment. The features can be stationary or moving objects in the environment. Features can include vehicles, people, animals, plants, obstacles, road surface irregularities, especially potholes or stones, road markings, traffic signs, open spaces, especially parking lots, precipitation, or the like.

The optical detection system 10 can provide an accurate, dense, and reliable point cloud of the detection area 32. The point cloud can show the contours of objects and/or other features. The data has been verified by fusing the first optical data with the second optical data, making the data more reliable and accurate. Furthermore, a confidence measure can be generated to provide an indication of the quality of the data.

The computing unit 34 can be designed as a central vehicle computer of the vehicle 30, in which data from multiple detection systems of the vehicle 30 can be received, evaluated, and/or further processed. The computing unit 34 can be used, for example, to implement autonomous or semi-autonomous driving functions.

3 schematically illustrates lists of candidate objects LS-21, LD-21 generated using the first optical data 21.

A first list of candidate objects LS-21 includes static candidate objects SOA-1, SOA-2, ..., SOA-n1 detected in the first optical data 21. A second list of candidate objects LD-21 includes dynamic candidate objects DOA-1, DOA-2, ..., DOA-m1 detected in the first optical data 21.

The lists of candidate objects LS-21, LD-21 are generated by the processor 20 by processing the first optical data 21 using a classification algorithm based on artificial intelligence, e.g., a trained neural network. The classification algorithm enables the detection of the dynamic and static objects DO-1, DO-2, ..., DO-m, SO-1, SO-2, ..., SO-n in the detection area 32.

4 schematically illustrates further lists of candidate objects LS-22, LD-22 generated using the second optical data 22.

A first further list of candidate objects LS-22 comprises static candidate objects SOP-1, SOP-2, ..., SOP-n2, which were detected in the second optical data 22. A second further list of candidate objects LD-22 comprises dynamic candidate objects DOP-1, DOP-2, ..., DOP-m2, which were detected in the second optical data 22.

The further lists of candidate objects LS-22, LD-22 are generated by the processor 20 by processing the second optical data 22 using image signal processing. The image signal processing enables the detection of the dynamic and static objects DO-1, DO-2, ..., DO-m, SO-1, SO-2, ..., SO-n in the detection area 32.

5 schematically illustrates the list of objects OL in the detection area 32. It includes a list SOL with static objects and a list DOL with dynamic objects. The list DOL with dynamic objects includes the dynamic static objects DO-1, DO-2, ..., DO-m in detection area 32. The list SOL with static objects includes the static objects SO-1, SO-2, ..., SOn in detection area 32.

For each object, it is defined whether it was detected in the first optical data 21 and/or in the second optical data 22. An identified object is marked with a "+." An unidentified object is marked with a "-." By using this list OL for comparison, object detection can be improved through cross-checking. For example, only objects detected in both the first and second optical data 21, 22 could be considered a verified object. Furthermore, a confidence measure could be generated from the DOL list with dynamic objects and the SOL list with static objects.

During real-time operation of the optical source 14 and the optical receiver sensor 12, a decision regarding the reliability of object detection must be made in real time. The use of the first and second optical data 21, 22 can support the assessment of this question and enables the assessment of the reliability and confidence of the information provided by the optical detection system 10. This supports the assessment of the reliability of the object information by enabling qualification and validation of the generated object lists LS-21, LD-21, LS-22, LD-22. The qualification and validation can be performed internally in the processor 20.

6 schematically illustrates an optical detection system 10 that carries out the described method for object detection, which includes self-validation.

The optical receiving sensor 12 is designed to perform both active and passive sensing in real time. The optical receiving sensor 12 is included in the real-time environment 16. The real-time environment also includes the optical source 14. The real-time environment 16 can be implemented on a single chip and can enable the processor 20 to perform object detection, including self-validation, in, for example, nanoseconds.

The optical receiving sensor 16 outputs the detected first and second optical data 21, 22 to the processor 20 as point cloud data 18. The point cloud data 18 includes the first optical data 21 and the second optical data 22.

In the next steps, the first optical data 21, which comprises active image data, e.g., lidar data, are processed separately from the second optical data 22, which comprises passive image data, e.g., grayscale image.

In step 102, the object detection in the detection area 32 is carried out using the first optical data 21.

In 102.1, preprocessing is performed on the first optical data 21. Preprocessing includes data cleaning to remove noise and outliers to improve the quality of the data for object detection. Preprocessing further includes downsampling to reduce the size of the data set to make processing more manageable, using techniques such as voxel grid downsampling.

In 102.2, clustering and feature extraction, such as edge detection, are performed on the output of 102.1. Clustering involves grouping points according to similarity, and feature extraction transforms the point data information into feature information. Features can be edges, corners, textures, or similar.

In 102.3, segmentation is performed on the output of 102.2. Segmentation involves partitioning the image data into multiple segments that potentially represent objects. The output of 102.3 is the candidate object lists LS-21 and LD-21.

In 104, the object detection in the detection area 32 is carried out using the second optical data 22.

In 104.1, image signal processing is performed on the second optical data 22.

In 104.2, clustering and feature extraction, such as edge detection, are performed on the output of 104.1. Clustering involves grouping points according to similarity, and feature extraction transforms the point data information into feature information. Features can be edges, corners, textures, or similar.

In 104.3, segmentation is performed on the output of 104.2. Segmentation involves partitioning the image data into multiple segments that potentially represent objects. The output of 104.3 is the candidate object lists LS-22 and LD-22.

In 106, the information obtained in 102 and 104 is merged. The fusion step 106 combines the lists LS-21, LD-21, LS-22, LD-22, which are related to 3 and 4 In 108, the information obtained in 106 is validated. The validation step 108 was carried out with reference to 5 described. The output of 108 is the list of detected and validated objects OL. The second optical data 22 can be output together with the list of detected and validated objects OL.

The described method and the described optical detection system 10 enable the reliability of the list of detected objects OL to be improved. Furthermore, performance can be improved by reducing false positives. Furthermore, it is possible to assess object detection qualitatively and quantitatively, e.g., by determining a confidence measure, such as a positive predictive value (PPV) or similar.

In particular, phantom objects can be identified using the described method. Phantom objects are objects that appear in the image data but do not exist in reality.

The quality of object detection can be further improved by applying a pixel-to-pixel quality comparison. For example, the same object should have the same pixel size in both the first and second optical data 21, 22. For example, if an object O has a size of 24 x 30 pixels in the passive image, the object O in the active image should also be 24 x 30 pixels. Any artifacts surrounding this pixel area with the real size can then be identified as noise. This can then be filtered, and the performance and quality of object detection can be improved.

QUOTES CONTAINED IN THE DESCRIPTION

This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• US 2016/0054434 [0006]
• US 2019/0353791A1 [0007]

Claims (14)

A method for object detection using first and second optical data (21, 22) of a detection area (32), wherein the first optical data (21) comprises active image data and the second optical data (22) comprises passive image data, the method comprising: receiving the first and second optical data (21, 22) from an optical receiving sensor (12), wherein the optical receiving sensor (12) comprises a plurality of pixels, the pixels being configured to provide the first and second optical data (21, 22) of the detection area (32); performing an object detection in the detection area (32) using the first and second optical data (21, 22). Procedure according to Claim 1 , further comprising outputting a list of detected objects (OL) and the second optical data (22). Procedure according to Claim 1 or 2 , wherein the object detection further comprises performing clustering, edge detection and/or segmentation on the first optical data (21) and separately on the second optical data (22). Method according to one of the preceding claims, wherein the object detection using the first optical data (21) comprises a classification step using artificial intelligence. Method according to one of the preceding claims, wherein the object detection using the second optical data (22) comprises image signal processing. Method according to one of the preceding claims, wherein the object detection using the first optical data (21) comprises generating at least one list of candidate objects (LS-21, LD-21) using the first optical data (21), and/or the object detection using the second optical data (22) comprises at least one further list of candidate objects (LS-22, LD-22) using the second optical data (22). Procedure according to Claim 6 , wherein the at least one list of candidate objects (LS-21, LD-21) comprises a list of dynamic candidate objects (LD-21) and a list of static candidate objects (LS-21) and/or the at least one further list of candidate objects (LS-22, LD-22) comprises a further list of dynamic candidate objects (LD-22) and a further list of static candidate objects (LS-22). Procedure according to Claim 6 or 7 , wherein the list of objects (OL) is generated using the at least one list of candidate objects (LS-21, LD-21) and the at least one further list of candidate objects (LS-22, LD-22). Method according to one of the Claims 6 until 8 , wherein a confidence measure is calculated and output using the at least one list of candidate objects (LS-21, LD-21) and the at least one further list of candidate objects (LS-22, LD-22). An optical detection system (10) comprising an optical receiving sensor (12), wherein the optical receiving sensor (12) comprises a plurality of pixels, the pixels being configured to provide first and second optical data (21, 22) of a detection area (32), wherein the first optical data (21) comprises active image data, wherein the second optical data (22) comprises passive image data, wherein the optical detection system (10) further comprises a processor (20) configured to perform object detection in the detection area (32) using the first and second optical data (21, 22). Optical detection system according to Claim 10 , further comprising an optical source (14) used to obtain the active image data, wherein the optical receive sensor (12) and the optical source (14) are contained in a real-time environment. Optical detection system according to Claim 10 or 11 , wherein the optical receiving sensor (12) and the optical source (14) are arranged on a single chip. Optical detection system according to one of the Claims 10 until 12 , wherein the processor (20) is designed to carry out the steps of the method according to one of the Claims 1 until 9 to carry out. Vehicle (30) comprising the optical detection system (10) according to one of the Claims 10 until 13 includes.