# US 12,217,473 B2 · Object detection device, object detection method, and storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,473 B2 - Document ID: US12217473B2 - Status: Granted - Kind: B2 - Application number: 17/702,828 - Filed: 2022-03-24 - Published: 2022-10-06 - Granted: 2025-02-04 - Earliest claimed: 2021-03-31 - Assignees: Honda Motor Co., Ltd. - Applicants: Honda Motor Co., Ltd. - Inventors: Yuji Yasui - Web page: https://patentgrep.com/patent/US12217473B2 ## CPC - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes} - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An object detection device includes a storage device which has stored a program, and a hardware processor, in which the hardware processor executes the program stored in the storage device, thereby acquiring a captured image in which a road is captured, generating a low-resolution image in which an image quality of the captured image is lowered, defining one or more partial area sets with each of the one or more partial area sets defined by cutting out a plurality of partial areas from the low-resolution image, deriving an aggregated value obtained by aggregating differences in feature amount between the partial areas included in each of the one or more partial area set and the peripheral partial areas, and extracting a point of interest based on the aggregated value. ## Claims ### Claim 1 An object detection device comprising: a storage device which storing a program; and a hardware processor, wherein the hardware processor executes the program stored in the storage device to: acquire a captured image in which a road is captured; generate a low-resolution image in which an image quality of the captured image is lowered; define a plurality of partial area sets in the low-resolution image, each of the plurality of partial area sets being a set of a plurality of partial areas, the number of pixels in each of the partial areas differing from each other between the plurality of partial area sets; derive an aggregated value obtained by aggregating differences in feature amount between the partial areas included in each of the plurality of partial area sets and the peripheral partial areas, and extract a point of interest based on the aggregated value; and cause a mobile object to autonomously travel based on a result of determination for an object in the point of interest in the captured image. ### Claim 2 (depends on 1) The object detection device according to claim 1, wherein the hardware processor extracts the point of interest by adding the aggregated value for each pixel between the plurality of partial area sets. ### Claim 3 (depends on 1) The object detection device according to claim 1, wherein the hardware processor derives the aggregated value by aggregating differences in feature amount between the partial areas included in each of the plurality of partial area sets and the other partial areas adjacent vertically, horizontally, and diagonally. ### Claim 4 (depends on 3) The object detection device according to claim 3, wherein the hardware processor further adds a difference in feature amount between the partial areas vertically adjacent, a difference in feature amount between the partial areas horizontally adjacent, and a difference in feature amount between the partial areas diagonally adjacent for the partial areas included in each of the plurality of partial area sets to the aggregated value. ### Claim 5 (depends on 1) The object detection device according to claim 1, wherein the hardware processor determines whether the object on a road is an object that the mobile object needs to avoid contact with by performing high-resolution processing on the point of interest in the captured image. ### Claim 6 An object detection method executed using a computer comprising: acquiring a captured image in which a road is captured; generating a low-resolution image in which an image quality of the captured image is lowered; defining a plurality of partial area sets in the low-resolution image, each of the plurality of partial area sets being a set of a plurality of partial areas, the number of pixels in each of the partial areas differing from each other between the plurality of partial area sets; deriving an aggregated value obtained by aggregating differences in feature amount between the partial areas included in each of the plurality of partial area sets and the peripheral partial areas, and extracting a point of interest based on the aggregated value; and causing a mobile object to autonomously travel based on a result of determination for an object in the point of interest in the captured image. ### Claim 7 A computer-readable non-transitory storage medium that has stored a program causing a computer to: acquire a captured image in which a road is captured; generate a low-resolution image in which an image quality of the captured image is lowered; define a plurality of partial area sets in the low-resolution image, each of the one or more plurality of partial area sets being a set of a plurality of partial areas, the number of pixels in each of the partial areas differing from each other between the plurality of partial area sets; derive an aggregated value obtained by aggregating differences in feature amount between the partial areas included in each of the plurality of partial area sets and the peripheral partial areas, and extract a point of interest based on the aggregated value; and cause a mobile object to autonomously travel based on a result of determination for an object in the point of interest in the captured image.