OBJECT DETECTION DEVICE, OBJECT DETECTION METHOD, AND OBJECT DETECTION PROGRAM

Abstract

It is possible to reduce the amount of calculation required for inference processing when point cloud data is used. [Solution] A point cloud acquisition unit 21 acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point. A difference calculation unit 23 identifies difference data that is the difference between the acquired point cloud data and a reference point cloud data. A region determination unit 24 determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data. An object detection unit 25 inputs the point cloud data for the detection region into an object detection model to acquire a detection result.

Description

[Technical field]

[0001] The present disclosure relates to a technique for detecting an object using point cloud data. [Background technology]

[0002] There is a technology that uses a visible light camera to detect objects such as abandoned property. When using a visible light camera to detect objects, the object cannot be detected properly in an environment where there is insufficient light, such as at night, or in an environment where direct sunlight shines on the visible light camera and causes flare.

[0003] There are optical sensors such as LiDAR that collect point cloud data by emitting light and receiving the reflected light reflected at a reflection point. LiDAR is an abbreviation for Light Detection and Ranging. Optical sensors can obtain point cloud data even in environments with insufficient light, such as at night, and in environments where direct sunlight shines on a visible light camera and causes flare. Therefore, detection of objects based on point cloud data obtained using optical sensors is being considered.

[0004] Patent Document 1 describes a method of comparing background point cloud data stored in a memory with current point cloud data, and identifying the appearance of an object if a difference occurs. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2020-118619 A Summary of the Invention [Problem to be solved by the invention]

[0006] Point cloud data obtained using optical sensors has a larger amount of data than image data obtained using visible light cameras. Therefore, when point cloud data is used as input to perform inference processing of a deep learning model that detects specific objects, the amount of calculation required for the inference processing becomes large, and expensive hardware is required. An object of the present disclosure is to reduce the amount of calculation required for inference processing when point cloud data is used. [Means for solving the problem]

[0007] The object detection device according to the present disclosure comprises: a point cloud acquisition unit that acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a difference specifying unit that specifies difference data that is a difference between the point cloud data acquired by the point cloud acquiring unit and reference point cloud data; a region determining unit that determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identifying unit; an object detection unit that inputs the point cloud data for the detection area determined by the area determination unit into an object detection model to obtain a detection result; Equipped with. Effect of the Invention

[0008] In the present disclosure, an area corresponding to a cluster area obtained by clustering difference data is determined as a detection area, and the detection area is input to an object detection model to obtain a detection result. This makes it possible to reduce the amount of data input to the object detection model, and to reduce the amount of calculation for inference processing when point cloud data is used. [Brief description of the drawings]

[0009] [Figure 1] 1 is a configuration diagram of an object detection device 10 according to a first embodiment. [Diagram 2] FIG. 3 is an explanatory diagram of a voxel definition 31 according to the first embodiment. [Diagram 3] 3 is a flowchart showing a process flow of the object detection device 10 according to the first embodiment. [Figure 4] FIG. 3 is an explanatory diagram of a voxelization process according to the first embodiment. [Diagram 5] FIG. 4 is an explanatory diagram of a difference identification process according to the first embodiment. [Figure 6] FIG. 2 is an explanatory diagram of a first method for determining a detection region 46 according to the first embodiment. [Figure 7] FIG. 2 is an explanatory diagram of a first method for determining a detection region 46 according to the first embodiment. [Figure 8] FIG. 4 is an explanatory diagram of a second method for determining a detection region 46 according to the first embodiment. [Figure 9] FIG. 11 is an explanatory diagram of a difference determination process according to the first modified example. [Figure 10] FIG. 11 is an explanatory diagram of a difference determination process according to Modification 2. [Figure 11] FIG. 11 is an explanatory diagram of an object detection process according to the second embodiment. [Figure 12] FIG. 11 is an explanatory diagram of an object detection process according to the third embodiment. [Figure 13] FIG. 13 is an explanatory diagram of an object detection process according to Modification 5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Embodiment 1 ***Configuration Description*** The configuration of an object detection device 10 according to the first embodiment will be described with reference to FIG. The object detection device 10 is a computer. The object detection device 10 includes the following hardware components: a processor 11, a memory 12, a storage 13, and a communication interface 14. The processor 11 is connected to other hardware components via signal lines and controls the other hardware components.

[0011] The processor 11 is an IC that performs processing. IC is an abbreviation for Integrated Circuit. Specific examples of the processor 11 include a CPU, a DSP, and a GPU. CPU is an abbreviation for Central Processing Unit. DSP is an abbreviation for Digital Signal Processor. GPU is an abbreviation for Graphics Processing Unit.

[0012] The memory 12 is a storage device that temporarily stores data. Specific examples of the memory 12 include SRAM and DRAM. SRAM is an abbreviation for Static Random Access Memory. DRAM is an abbreviation for Dynamic Random Access Memory.

[0013] The storage 13 is a storage device that stores data. Specific examples of the storage 13 include an SSD or an HDD. SSD stands for Solid State Drive. HDD stands for Hard Disk Drive. The storage 13 may also be a portable recording medium such as an SD (registered trademark) memory card, CompactFlash (registered trademark), NAND flash, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a DVD. SD stands for Secure Digital. DVD stands for Digital Versatile Disk.

[0014] The communication interface 14 is an interface for communicating with an external device. Specific examples of the communication interface 14 include Ethernet (registered trademark), USB, and HDMI (registered trademark) ports. USB is an abbreviation for Universal Serial Bus. HDMI is an abbreviation for High-Definition Multimedia Interface.

[0015] The object detection device 10 is connected to an optical sensor 40 via the communication interface 14. The optical sensor 40 is a device that acquires point cloud data 41 by irradiating a target area with irradiation light and receiving the reflected light reflected at a reflection point. The position of each point in the point cloud data 41 is identified based on the irradiation angle of the irradiation light and the time from when the irradiation light is irradiated to when the reflected light is received.

[0016] The object detection device 10 includes, as functional components, a point cloud acquisition unit 21, a voxelization unit 22, a difference identification unit 23, a region determination unit 24, and an object detection unit 25. The functions of the functional components of the object detection device 10 are realized by software. The storage 13 stores a program that realizes the function of each functional component of the object detection device 10. The program is loaded into the memory 12 by the processor 11 and executed by the processor 11. In this way, the function of each functional component of the object detection device 10 is realized.

[0017] The storage 13 also stores a voxel definition 31 .

[0018] 1 shows only one processor 11. However, there may be a plurality of processors 11, and the plurality of processors 11 may cooperate to execute programs that realize the respective functions.

[0019] ***Explanation of Operation*** The operation of the object detection device 10 according to the first embodiment will be described with reference to FIGS. The operation procedure of the object detection device 10 according to the embodiment 1 corresponds to the object detection method according to the embodiment 1. Moreover, the program for realizing the operation of the object detection device 10 according to the embodiment 1 corresponds to the object detection program according to the embodiment 1.

[0020] The voxel definition 31 according to the first embodiment will be described with reference to FIG. The voxel definition 31 is data that defines the voxels 32. The voxels 32 are three-dimensional regions obtained by dividing the target region 33. The size of the voxels 32 can be set arbitrarily according to the position in the target region 33. In the first embodiment, the larger the distance from the optical sensor 40, the larger the size of the voxels 32 defined.

[0021] The flow of processing performed by the object detection device 10 according to the first embodiment will be described with reference to FIG. (Step S11: Point cloud acquisition process) The point cloud acquisition unit 21 acquires point cloud data 41 obtained by irradiating the target area 33 with light using an optical sensor 40 and receiving light reflected at a reflection point.

[0022] (Step S12: Voxelization process) The voxelization unit 22 generates voxel data 42 from the point cloud data 41 acquired in step S11. A specific description will be given with reference to Fig. 4. The voxelization unit 22 sets each point of the point cloud data 41 acquired in step S11 as a target point. The voxelization unit 22 associates the target point with a voxel 32 that includes the position of the target point among a plurality of voxels 32 obtained by dividing the target region 33. In this way, voxel data 42 corresponding to the point cloud data 41 is generated.

[0023] (Step S13: Difference identification process) The difference identification unit 23 identifies difference data 44, which is the difference between the point cloud data acquired in step S11 and the reference point cloud data. Here, the difference identification unit 23 compares the voxelized point cloud data to generate the difference data 44. The data obtained by voxelizing the reference point cloud data is called reference data 43. In other words, the difference identification unit 23 identifies difference data 44 that is a difference between the voxel data 42 and the reference data 43. In the first embodiment, the reference data 43 is voxel data 42 generated from the background point cloud data. That is, the reference data 43 is voxel data 42 generated by performing the process of step S12 on the background point cloud data. The background point cloud data is point cloud data 41 acquired by the optical sensor 40 in a state where no object or the like is present in the target region 33.

[0024] Specifically, the difference identification unit 23 sets each of the multiple voxels 32 as a target voxel 32. The difference identification unit 23 determines whether or not there is a difference between the voxel data 42 and the reference data 43 for the target voxel 32, based on whether or not there is a corresponding point, that is, based on the presence or absence of a point in the voxel 32. If there is a difference, the difference identification unit 23 identifies the voxel 32 as difference data 44. 5, when a target voxel 32 has no points associated with it in the reference data 43 and has points associated with it in the voxel data 42, the difference identification unit 23 identifies it as difference data 44. In other words, the difference identification unit 23 does not identify a voxel 32 with points associated with it in the reference data 43 as difference data 44 even if the number of associated points is different.

[0025] (Step S14: Clustering process) The difference identifying unit 23 clusters the difference data 44 identified in step S13 to form a cluster region 45. The clustering method may use an existing clustering technique, such as grouping adjacent difference data 44 into the same cluster.

[0026] (Step S15: Area determination process) The region determining unit 24 determines a region corresponding to the cluster region 45 obtained in step S14 as the detection region 46. Here, one or more detection regions 46 are set. The method of determining the detection region 46 will be described later.

[0027] (Step S16: Object detection process) The object detection unit 25 sets each of the one or more detection regions 46 determined in step S15 as a target detection region 46. The object detection unit 25 inputs the point cloud data 41 for the target detection region 46 to the object detection model, and acquires a detection result that is an inference result. That is, the object detection unit 25 inputs the point cloud data 41 for the detection region 46, out of the point cloud data 41 acquired in step S21, to the object detection model. Then, the object detection unit 25 acquires a detection result that is a result of inference performed by the object detection model. Note that the object detection unit 25 may input the difference data 44 for the detection region 46, out of the difference data 44 identified in step S13, to the object detection model.

[0028] (Step S17: Result integration process) The object detection unit 25 generates result data by integrating the detection results for each of the one or more detection areas 46 acquired in step S16. Then, the object detection unit 25 outputs the result data.

[0029] The area determination process (step S15 in FIG. 3) according to the first embodiment will be described. Here, two methods, method 1 and method 2, will be described as methods for determining the detection area 46.

[0030] Method 1 will be described with reference to FIG. The region determining unit 24 determines an extended region 47 that is an extension of the cluster region 45 by a reference range as the detection region 46 . Here, the reference range may be set uniformly in all axial directions, or may be set for each axial direction. For example, the reference range may be set to 6 in the x-axis direction, 8 in the y-axis direction, and 10 in the z-axis direction. In this case, if the size of the cluster region 45 is 4 in the x-axis direction, 5 in the y-axis direction, and 6 in the z-axis direction, the detection region 46 is expanded by 6 in the x-axis direction to a width of 10 in the x-axis direction, expanded by 8 in the y-axis direction to a width of 13 in the y-axis direction, and expanded by 10 in the z-axis direction to a width of 16 in the y-axis direction. In this case, the region is expanded by 3 on each side in the x-axis direction, by 4 on each side in the y-axis direction, and by 5 on each side in the z-axis direction. The reference range may be set so that it is determined for each axis direction according to the size of cluster region 45 in that axis direction. For example, the reference range may be set to twice the size of cluster region 45. In this case, if the size of cluster region 45 in the x-axis direction is 5, it is expanded by 10 in the x-axis direction, and an area with a width of 15 in the x-axis direction becomes detection region 46. In this case, cluster region 45 is expanded by 5 on each side in the x-axis direction to become detection region 46. The size in the y-axis and z-axis directions is also doubled to match the axial directions.

[0031] Assume that there are a plurality of cluster regions 45 as shown in Fig. 7. Assume also that a portion of an extended region 47 expanded by a reference range around the plurality of cluster regions 45 overlaps with the other cluster regions 45. In this case, the region determination unit 24 may determine a merged region obtained by merging the extended regions 47 for the plurality of cluster regions 45 as the detection region 46. In other words, the region determination unit 24 combines the extended regions 47 and determines the region including the extended region 47 as the detection region 46.

[0032] Method 2 will now be described with reference to FIG. Of the multiple divided regions 34 obtained by dividing the target region 33 based on the distance from the optical sensor 40, the region determination unit 24 determines the divided region 34 that includes the reference position of the cluster region 45 as the detection region 46. Here, as a specific example, the reference position of cluster region 45 is the center of cluster region 45. For example, in Fig. 8, target region 33 is divided into divided regions 34 of areas a to d. And the center of cluster region 45 is included in area b. Therefore, area b is determined as detection region 46. The reference position of the cluster region 45 may be at least a part of the cluster region 45. In this case, if the cluster region 45 spans multiple divided regions 34, the region obtained by combining the multiple divided regions 34 is determined as the detection region 46. 8, the target area 33 is divided horizontally to obtain a plurality of divided areas 34, but the target area 33 may be divided vertically to obtain a plurality of divided areas 34. In this case as well, one or a plurality of divided areas 34 including a cluster area 45 are determined as the detection area 46.

[0033] ***Advantages of the First Embodiment*** As described above, the object detection device 10 according to the first embodiment determines an area corresponding to the cluster area 45 obtained by clustering the difference data 44 as the detection area 46, and inputs the detection area 46 to the object detection model to obtain a detection result. This makes it possible to suppress the amount of data input to the object detection model, and to reduce the amount of calculation in the inference process when the point cloud data 41 is used. That is, normally, point cloud data for the entire target area 33 is input to the object detection model. In contrast, only point cloud data for the detection area 46 of the target area 33 is input to the object detection model. This makes it possible to reduce the amount of calculation required for inference processing.

[0034] By reducing the amount of data input to the object detection model, it is possible to reduce the amount of communication and data storage capacity required for input to the object detection model.

[0035] ***Other configurations*** <Variation 1> In step S13 of FIG. 3, the difference calculation unit 23 may exclude from the difference data 44 the voxels 32 in which the number of associated points in the voxel data is equal to or less than a reference number of points. For example, the number of reference points is set to 2. In this case, as shown in Fig. 9, voxels 32 in which the number of associated points in the voxel data is two or less are excluded from the difference data 44. In Fig. 9, the middle voxel 32 has two associated points and is therefore excluded from the difference data 44. On the other hand, the bottom voxel 32 has three associated points and is therefore left in the difference data 44.

[0036] <Variation 2> In step S14 of FIG. 3, the difference identification unit 23 may exclude from the difference data 44 the voxels 32 that constitute the obtained cluster region 45 in which the number of voxels 32 included in the cluster region 45 is equal to or smaller than the first voxel number. For example, the first voxel number is set to 5. In this case, as shown in Fig. 10, the voxels 32 constituting cluster region 45B, which has 4 voxels 32, are excluded from the difference data 44. On the other hand, the voxels 32 constituting cluster A, which has 10 voxels 32, are left as the difference data 44.

[0037] <Modification 3> In the first embodiment, each functional component is realized by software. However, as a third modification, each functional component may be realized by hardware. The following describes the third modification, focusing on the differences from the first embodiment.

[0038] When each functional component is realized by hardware, the object detection device 10 includes an electronic circuit 15 instead of the processor 11, the memory 12, and the storage 13. The electronic circuit 15 is a dedicated circuit for realizing the functions of each functional component, the memory 12, and the storage 13.

[0039] The electronic circuit 15 may be a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a logic IC, a GA, an ASIC, or an FPGA. GA is an abbreviation for Gate Array. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field-Programmable Gate Array. Each functional component may be realized by one electronic circuit 15, or each functional component may be distributed across multiple electronic circuits 15.

[0040] <Modification 4> As a fourth modification, some of the functional components may be realized by hardware, and other functional components may be realized by software.

[0041] The processor 11, the memory 12, the storage 13, and the electronic circuit 15 are collectively referred to as a processing circuit. In other words, the functions of the respective functional components are realized by the processing circuit.

[0042] Embodiment 2 The second embodiment differs from the first embodiment in that a different object detection model is used depending on the distance from the optical sensor 40 to the detection area 46. In the second embodiment, this difference will be described, and a description of the same points will be omitted.

[0043] ***Explanation of Operation*** The object detection process (step S16 in FIG. 3) according to the second embodiment will be described with reference to FIG. In the second embodiment, a plurality of object detection models are prepared according to the distance from the optical sensor 40. In Fig. 11, an object detection model A corresponding to areas a and b close to the optical sensor 40, and an object detection model B corresponding to areas c and d far from the optical sensor 40 are prepared. Here, each object detection model is trained using point cloud data 41 of the corresponding distance. Trained using point cloud data 41 of the corresponding distance means trained using point cloud data 41 with a density according to the corresponding distance. That is, in the example of FIG. 11, object detection model A is trained using point cloud data 41 of areas a and b which are close to the optical sensor 40. Object detection model B is trained using point cloud data 41 of areas c and d which are far from the optical sensor 40.

[0044] The object detection unit 25 sets each of the one or more detection regions 46 determined in step S15 as the target detection region 46. The object detection unit 25 inputs the point cloud data 41 for the target detection region 46 to an object detection model according to the distance from the optical sensor 40 to the target detection region 46 to obtain a detection result. In the example of FIG. 11, when the target detection region 46 is included in area a or area b, the object detection unit 25 inputs the point cloud data 41 for the target detection region 46 to the object detection model A. On the other hand, when the target detection region 46 is included in area c or area d, the object detection unit 25 inputs the point cloud data 41 for the target detection region 46 to the object detection model B.

[0045] ***Effects of the second embodiment*** As described above, the object detection device 10 according to the second embodiment uses different object detection models depending on the distance from the optical sensor 40 to the detection region 46. The density of the point cloud data 41 changes depending on the distance from the optical sensor 40. That is, the closer an area is to the optical sensor 40, the higher the density of the point cloud data 41. Therefore, even for the same object, the appearance of the point cloud data 41 differs greatly depending on the distance from the optical sensor 40. As a result, when attempting to detect objects in a wide range, the detection accuracy of the object detection model decreases. For example, it becomes easy for an object that can be detected in an area close to the optical sensor 40 to not be detected in an area far from the optical sensor 40. The object detection device 10 according to the second embodiment can improve detection accuracy by using an object detection model according to the distance of the detection area 46 from the optical sensor 40. In particular, each object detection model is trained using the point cloud data 41 of the corresponding distance. Therefore, when the point cloud data 41 of the corresponding distance is provided as an input, it is possible to detect an object with high accuracy.

[0046] Embodiment 3 The third embodiment differs from the first and second embodiments in that whether or not to use an object detection model is controlled depending on the position of the cluster region 45. In the third embodiment, this difference will be described, and a description of the same points will be omitted. In the third embodiment, a case where a modification is made to the second embodiment will be described. However, it is also possible to make modifications to the second embodiment.

[0047] ***Explanation of Operation*** The object detection process (step S16 in FIG. 3) according to the third embodiment will be described with reference to FIG. In the third embodiment, the target area 33 is divided into an area that uses the object detection model and an area that does not use the object detection model. For example, in the target area 33, a close area that is less than a reference distance from the optical sensor 40 is set as an area that uses the object detection model, and a far area that is the reference distance or more from the optical sensor 40 is set as an area that does not use the object detection model. In Fig. 12, areas a and b that are close to the optical sensor 40 are areas that use the object detection model, and areas c and d that are far from the optical sensor 40 are areas that do not use the object detection model.

[0048] The object detection unit 25 sets each of the one or more detection regions 46 determined in step S15 as the target detection region 46. The object detection unit 25 determines whether or not to use an object detection model depending on the position of the cluster region 45 that is the basis of the target detection region 46. When the object detection model is used, the object detection unit 25 inputs the point cloud data 41 for the target detection region 46 into the object detection model to obtain a detection result. On the other hand, when the object detection model is not used, the object detection unit 25 sets the cluster region 45 that is the basis of the target detection region 46 as the detection result of the target detection region 46.

[0049] ***Effects of the Third Embodiment*** As described above, object detection device 10 according to embodiment 3 controls whether or not to use an object detection model depending on the position of cluster region 45. In particular, object detection device 10 according to embodiment 3 does not use an object detection model for regions far from optical sensor 40, and takes cluster region 45 as the detection result as it is. As a result, for regions far from optical sensor 40 where it is difficult to detect an object using the object detection model, it is possible to take a portion where an object may exist as the detection result without using an object detection model.

[0050] ***Other configurations*** <Variation 5> In the third embodiment, a modification is made to the first embodiment. However, it is also possible to make a modification to the second embodiment. A modification to the second embodiment will be described with reference to FIG.

[0051] The target area 33 is divided into three or more areas according to the distance from the optical sensor 40, and the farthest area is set as an area in which an object detection model is not used. For the remaining areas, an object detection model is prepared for each area. 13, area d, which is the farthest from the optical sensor 40, is set as an area in which no object detection model is used. An object detection model A corresponding to areas a and b, which are close to the optical sensor 40, and an object detection model B corresponding to area c, which is far from the optical sensor 40, are prepared.

[0052] The object detection unit 25 sets each of the one or more detection regions 46 determined in step S15 as the target detection region 46. The object detection unit 25 determines whether or not to use an object detection model depending on the position of the cluster region 45 that is the basis of the target detection region 46. When using an object detection model, the object detection unit 25 inputs the object detection model according to the distance from the optical sensor 40 to the target detection region 46 to obtain a detection result. On the other hand, when not using an object detection model, the object detection unit 25 sets the cluster region 45 that is the basis of the target detection region 46 as the detection result of the target detection region 46.

[0053] In addition, the word "part" in the above description may be read as a "circuit," "step," "procedure," "processing," or "processing circuit."

[0054] Various aspects of the present disclosure are summarized below as appendices. (Appendix 1) a point cloud acquisition unit that acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a difference specifying unit that specifies difference data that is a difference between the point cloud data acquired by the point cloud acquiring unit and reference point cloud data; a region determining unit that determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identifying unit; an object detection unit that inputs the point cloud data for the detection area determined by the area determination unit into an object detection model to obtain a detection result; An object detection device comprising: (Appendix 2) The region determination unit determines an extended region obtained by expanding the cluster region by a reference range as the detection region. 2. An object detection device as described in claim 1. (Appendix 3) When a plurality of cluster regions exist and an extended region expanded by a reference range partially overlaps between the plurality of cluster regions, the region determination unit determines a merged region obtained by merging the extended regions of the plurality of cluster regions as the detection region. 3. An object detection device as described in claim 2. (Appendix 4) The region determination unit determines, as the detection region, a divided region including a reference position of the cluster region, among a plurality of divided regions obtained by dividing the target region based on a distance from an optical sensor that irradiates the light. 2. An object detection device as described in claim 1. (Appendix 5) The object detection unit inputs the detection region to an object detection model corresponding to a reference position of the cluster region among a plurality of object detection models. 5. An object detection device according to any one of claims 1 to 4. (Appendix 6) Each of the plurality of object detection models is trained using point cloud data having a density corresponding to a reference position of the cluster region. 6. An object detection device as described in claim 5. (Appendix 7) The object detection unit treats a cluster area, which includes a reference position, as an output of the object detection model, among a plurality of divided areas obtained by dividing the area determination unit based on a distance from the optical sensor that irradiates the light, and which is a divided area whose distance from the optical sensor is equal to or greater than a reference distance. 7. An object detection device according to any one of claims 1 to 6. (Appendix 8) The object detection device further comprises: a voxelization unit that generates voxel data corresponding to the point cloud data by associating each point of the point cloud data acquired by the point cloud acquisition unit with a voxel that includes a position of the target point among a plurality of voxels obtained by dividing the target area, the voxelization unit generating voxel data corresponding to the point cloud data; The difference identification unit identifies, for each of the plurality of voxels, a difference cell having a difference between the voxel data generated from the point cloud data by the voxel generation unit and the voxel data generated from the reference point cloud data by the voxel generation unit, as the difference data, based on the presence or absence of a corresponding point. 8. An object detection device according to any one of claims 1 to 7. (Appendix 9) A computer acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; The computer identifies difference data which is a difference between the acquired point cloud data and a reference point cloud data, The computer determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data; An object detection method in which a computer inputs the point cloud data for the detection area into an object detection model to obtain a detection result. (Appendix 10) A point cloud acquisition process for acquiring point cloud data obtained by irradiating a target area with light and receiving reflected light reflected at a reflection point; A difference identification process that identifies difference data that is a difference between the point cloud data acquired by the point cloud acquisition process and reference point cloud data; a region determination process for determining, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identification process; an object detection process in which the point cloud data for the detection area determined by the area determination process is input to an object detection model to obtain a detection result; An object detection program that causes a computer to function as an object detection device that performs the above-mentioned steps.

[0055] The above describes the embodiments and modifications of the present disclosure. Some of these embodiments and modifications may be combined and implemented. Also, one or some of them may be partially implemented. Note that the present disclosure is not limited to the above embodiments and modifications, and various modifications are possible as necessary. [Explanation of symbols]

[0056] 10 object detection device, 11 processor, 12 memory, 13 storage, 14 communication interface, 21 point cloud acquisition unit, 22 voxelization unit, 23 difference identification unit, 24 area determination unit, 25 object detection unit, 31 voxel definition, 32 voxel, 33 target area, 34 divided area, 40 optical sensor, 41 point cloud data, 42 voxel data, 43 reference data, 44 difference data, 45 cluster area, 46 detection area, 47 extension area.

Claims

1. a point cloud acquisition unit that acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a difference specifying unit that specifies difference data that is a difference between the point cloud data acquired by the point cloud acquiring unit and reference point cloud data; a region determination unit that determines, as a detection region, a divided region that includes a reference position of a cluster region obtained by clustering the difference data identified by the difference identification unit, from among a plurality of divided regions obtained by dividing the target region based on a distance from an optical sensor that irradiates the light; an object detection unit that inputs the point cloud data for the detection area determined by the area determination unit into an object detection model to obtain a detection result; An object detection device comprising:

2. a point cloud acquisition unit that acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a difference specifying unit that specifies difference data that is a difference between the point cloud data acquired by the point cloud acquiring unit and reference point cloud data; a region determining unit that determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identifying unit; an object detection unit that inputs the point cloud data for the detection area determined by the area determination unit into an object detection model corresponding to a reference position of the cluster area among a plurality of object detection models to obtain a detection result; An object detection device comprising:

3. a point cloud acquisition unit that acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a difference specifying unit that specifies difference data that is a difference between the point cloud data acquired by the point cloud acquiring unit and reference point cloud data; a region determining unit that determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identifying unit; an object detection unit that inputs the point cloud data for the detection area determined by the area determination unit into an object detection model to obtain a detection result, and treats a cluster area, among a plurality of divided areas obtained by dividing the target area based on a distance from an optical sensor that irradiates the light, in which a reference position is included in a divided area whose distance from the optical sensor is equal to or greater than a reference distance, as an output of the object detection model; An object detection device comprising:

4. a point cloud acquisition unit that acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a voxelization unit that generates voxel data corresponding to the point cloud data by associating each point of the point cloud data acquired by the point cloud acquisition unit with a voxel that includes a position of the target point among a plurality of voxels obtained by dividing the target region, the voxelization unit treating each point of the point cloud data acquired by the point cloud acquisition unit as a target point; a difference identification unit that identifies, for each of the plurality of voxels, a difference cell having a difference between the voxel data generated from the point cloud data by the voxel generation unit and the voxel data generated from a reference point cloud data by the voxel generation unit, as difference data, based on the presence or absence of a corresponding point; a region determining unit that determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identifying unit; an object detection unit that inputs the point cloud data for the detection area determined by the area determination unit into an object detection model to obtain a detection result; An object detection device comprising:

5. The region determination unit determines an extended region obtained by expanding the cluster region by a reference range as the detection region. The object detection device according to any one of claims 1 to 4.

6. When a plurality of cluster regions exist and an extended region expanded by a reference range partially overlaps between the plurality of cluster regions, the region determination unit determines a merged region obtained by merging the extended regions of the plurality of cluster regions as the detection region. The object detection device according to claim 5 .

7. Each of the plurality of object detection models is trained using point cloud data having a density corresponding to a reference position of the cluster region. The object detection device according to claim 2 .

8. A computer acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; The computer identifies difference data which is a difference between the acquired point cloud data and a reference point cloud data, a computer determines, as a detection area, a divided area including a reference position of a cluster area obtained by clustering the difference data, from among a plurality of divided areas obtained by dividing the target area based on a distance from an optical sensor that irradiates the light; An object detection method in which a computer inputs the point cloud data for the detection area into an object detection model to obtain a detection result.

9. A computer acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; The computer identifies difference data which is a difference between the acquired point cloud data and a reference point cloud data, The computer determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data; An object detection method in which a computer inputs the point cloud data for the detection area into an object detection model corresponding to a reference position of the cluster area, out of a plurality of object detection models, to obtain a detection result.

10. A computer acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; The computer identifies difference data which is a difference between the acquired point cloud data and a reference point cloud data, The computer determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data; a computer inputting the point cloud data for the detection region into an object detection model to obtain a detection result; An object detection method in which a computer divides the target area based on the distance from an optical sensor that irradiates the light, and among the multiple divided areas obtained, for which a reference position is included in a divided area whose distance from the optical sensor is equal to or greater than a reference distance, treats the cluster area as an output of the object detection model.

11. A computer acquires point cloud data obtained by irradiating a target area with light and receiving light reflected at a reflection point; a computer generates voxel data corresponding to the point cloud data by associating each point of the point cloud data with a voxel that includes a position of the target point among a plurality of voxels obtained by dividing the target region, the voxel data being corresponding to the point cloud data; the computer identifies, for each of the plurality of voxels, a difference cell having a difference between the voxel data generated from the point cloud data and the voxel data generated from a reference point cloud data, as difference data, based on the presence or absence of a corresponding point; The computer determines, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data; An object detection method in which a computer inputs the point cloud data for the detection area into an object detection model to obtain a detection result.

12. A point cloud acquisition process for acquiring point cloud data obtained by irradiating a target area with light and receiving reflected light reflected at a reflection point; A difference identification process that identifies difference data that is a difference between the point cloud data acquired by the point cloud acquisition process and reference point cloud data; a region determination process for determining, as a detection region, a divided region including a reference position of a cluster region obtained by clustering the difference data identified by the difference identification process, out of a plurality of divided regions obtained by dividing the target region based on a distance from an optical sensor that irradiates the light; an object detection process in which the point cloud data for the detection area determined by the area determination process is input to an object detection model to obtain a detection result; An object detection program that causes a computer to function as an object detection device that performs the above-mentioned steps.

13. A point cloud acquisition process for acquiring point cloud data obtained by irradiating a target area with light and receiving reflected light reflected at a reflection point; A difference identification process that identifies difference data that is a difference between the point cloud data acquired by the point cloud acquisition process and reference point cloud data; a region determination process for determining, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identification process; an object detection process in which the point cloud data for the detection region determined by the region determination process is input to an object detection model corresponding to a reference position of the cluster region among a plurality of object detection models to obtain a detection result; An object detection program that causes a computer to function as an object detection device that performs the above-mentioned steps.

14. A point cloud acquisition process for acquiring point cloud data obtained by irradiating a target area with light and receiving reflected light reflected at a reflection point; A difference identification process that identifies difference data that is a difference between the point cloud data acquired by the point cloud acquisition process and reference point cloud data; a region determination process for determining, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identification process; an object detection process in which the point cloud data for the detection area determined by the area determination process is input to an object detection model to obtain a detection result, and in which, among a plurality of divided areas obtained by dividing the target area based on a distance from an optical sensor that irradiates the light, a cluster area in which a reference position is included in a divided area whose distance from the optical sensor is equal to or greater than a reference distance is treated as an output of the object detection model; An object detection program that causes a computer to function as an object detection device that performs the above-mentioned steps.

15. A point cloud acquisition process for acquiring point cloud data obtained by irradiating a target area with light and receiving reflected light reflected at a reflection point; a voxelization process in which each point of the point cloud data acquired by the point cloud acquisition process is regarded as a target point, and the target point is associated with a voxel that includes the position of the target point among a plurality of voxels obtained by dividing the target area, thereby generating voxel data corresponding to the point cloud data; a difference identification process for identifying, as difference data, difference cells having a difference between the voxel data generated from the point cloud data by the voxelization process and the voxel data generated from a reference point cloud data by the voxelization process, based on the presence or absence of a corresponding point for each of the plurality of voxels; a region determination process for determining, as a detection region, a region corresponding to a cluster region obtained by clustering the difference data identified by the difference identification process; an object detection process in which the point cloud data for the detection area determined by the area determination process is input to an object detection model to obtain a detection result; An object detection program that causes a computer to function as an object detection device that performs the above-mentioned steps.

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