A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.

A speed detecting method, applied to a speed detecting apparatus comprising a distance computing apparatus and a speed computing apparatus, the speed detecting method comprising: (a) computing an object to be detected distance between the speed detecting apparatus and an objected to be detected via the distance computing apparatus; and (b) computing a moving speed of the object to be detected according to the object to be detected distance via the speed computing apparatus.

Methods and systems for facilitating photo-based estimation are described. In an aspect, a computing device is configured to receive, from the camera, a signal comprising image data. The image data may represent at least a portion of a vehicle. The computing device may also retrieve data representing a preferred scene of the vehicle and determine, based on the image data and based on the data representing the preferred scene of the vehicle, whether the image data corresponds to the preferred scene of the vehicle. The server may, when the received image data corresponds to the preferred scene of the vehicle, enable capture of the image data. The server may send, via the communications module, a signal representing the captured image data to a processing server configured to analyze the captured image data to assess vehicular damage.

An image processing apparatus detects a subject of a first type and a subject of a second type to an image. When executing tracking processing of a subject based on a detection result of the detection circuit, if a same subject is detected as a subject of the first type and a subject of the second type, the image processing apparatus selects either the detection result regarding the subject of the first type or the detection result regarding the subject of the second type is to be used to perform the tracking processing of the subject.

Systems, methods, and devices for detecting brake lights are disclosed herein. A system includes a mode component, a vehicle region component, and a classification component. The mode component is configured to select a night mode or day mode based on a pixel brightness in an image frame. The vehicle region component is configured to detect a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode. The classification component is configured to classify a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.

A method for road surface detection includes receiving ranging data including a plurality of ranging data points, extracting one or more ranging data points lying within a height range from the plurality of ranging data points, dividing the one or more ranging data points into one or more grid cells, setting a first horizontal position of a first cell point of a first grid cell of the one or more grid cells as being centered on the first grid cell, setting a first vertical position of the first cell point, and detecting the road surface based on the first vertical position and first horizontal position of the first cell point.

A system for creating synthetic data for testing an autonomous system, comprising at least one hardware processor adapted to execute a code for: using a machine learning model to compute a plurality of depth maps based on a plurality of real signals captured simultaneously from a common physical scene, each of the plurality of real signals are captured by one of a plurality of sensors, each of the plurality of computed depth maps qualifies one of the plurality of real signals; applying a point of view transformation to the plurality of real signals and the plurality of depth maps, to produce synthetic data simulating a possible signal captured from the common physical scene by a target sensor in an identified position relative to the plurality of sensors; and providing the synthetic data to at least one testing engine to test an autonomous system comprising the target sensor.

The present disclosure relates to a vision inspection system based on deep learning and a vision inspection method of. The vision inspection system based on deep learning according to the present disclosure includes a GT generation module that generates a GT for a region of interest of a car part image, a learning module that receives learning data from the GT generation module, performs learning based on deep learning, and outputs a weight file, and an interface module that detects a defect with respect to an image file received from a vision program by using the weight file, and returns a defect detection result to the vision program.

An image processing system includes an image acquisition unit that acquires a plurality of images including a moving object image, an image capturing direction data calculation unit that calculates image capturing direction data indicating an image capturing direction in which an imaging device captures an image of a moving object at a time when the images is captured, a feature amount calculation unit that calculates a feature amount of the moving object image extracted from the images, and an associating unit that associates the moving objects in the images with each other based on the image capturing direction data and the feature amount.

The present invention provides an image processing apparatus for evaluating a surface of an object using a marker including marks whose positional relationship in a case where the marker is arranged in plane is known, the maker being arranged on the surface of the object. The image processing apparatus includes: a first acquisition unit configured to obtain image data obtained by capturing an image of the surface and an image of the marker arranged on the surface; a second acquisition unit configured to obtain information indicating positional relationship among the marks included in the marker; an extraction unit configured to extract the positional relationship among the marks included in the marker in an image indicated by the image data; and a correction unit configured to correct the image data based on the positional relationship indicated by the information and the extracted positional relationship.