Provided is an imaging device including: an imaging unit (130) that generates a one frame image by sequentially receiving each reflected light reflected by a subject by intermittently and sequentially irradiating the subject with each irradiation light having a different wavelength according to a position of the moving subject, temporarily and sequentially holding signal information based on the reflected light of each wavelength, and collectively reading the held signal information; and a combining unit (140) that generates a combined image by cutting a subject image corresponding to the reflected light of each wavelength from the one frame image and superimposing a plurality of the cut subject images.

Provided is an imaging device including: an imaging unit (130) that generates a one frame image by sequentially receiving each reflected light reflected by a subject by intermittently and sequentially irradiating the subject with each irradiation light having a different wavelength according to a position of the moving subject, temporarily and sequentially holding signal information based on the reflected light of each wavelength, and collectively reading the held signal information; and a combining unit (140) that generates a combined image by cutting a subject image corresponding to the reflected light of each wavelength from the one frame image and superimposing a plurality of the cut subject images.

A target recognition device includes a roadside object determination unit, a coordinate calculation unit, a road boundary estimation unit, a distance calculation unit, and a likelihood increasing unit. The roadside object determination unit determines whether a target has a feature of a roadside object. The coordinate calculation unit calculates a coordinate in a lateral direction of the target. The road boundary estimation unit estimates a road boundary. The distance calculation unit calculates a distance from the target to the road boundary. The likelihood increasing unit increases a likelihood that the target is a roadside object, on condition that the distance is a preset threshold value or less.

A target recognition device includes a roadside object determination unit, a coordinate calculation unit, a road boundary estimation unit, a distance calculation unit, and a likelihood increasing unit. The roadside object determination unit determines whether a target has a feature of a roadside object. The coordinate calculation unit calculates a coordinate in a lateral direction of the target. The road boundary estimation unit estimates a road boundary. The distance calculation unit calculates a distance from the target to the road boundary. The likelihood increasing unit increases a likelihood that the target is a roadside object, on condition that the distance is a preset threshold value or less.

A system and method for determining a dimension of a target. The method includes: determining a camera parameter, the camera parameter including at least one of a focal length, a yaw angle, a roll angle, a pitch angle, or a height of one or more cameras; acquiring a first image and a second image of an target captured by the one or more cameras; generating a first corrected image and a second corrected image by correcting the first image and the second image; determining a parallax between a pixel in the first corrected image and a corresponding pixel in the second corrected image; determining an outline of the target; and determining a dimension of the target based at least in part on the camera parameter, the parallax, and the outline of the target.

A system and method for determining a dimension of a target. The method includes: determining a camera parameter, the camera parameter including at least one of a focal length, a yaw angle, a roll angle, a pitch angle, or a height of one or more cameras; acquiring a first image and a second image of an target captured by the one or more cameras; generating a first corrected image and a second corrected image by correcting the first image and the second image; determining a parallax between a pixel in the first corrected image and a corresponding pixel in the second corrected image; determining an outline of the target; and determining a dimension of the target based at least in part on the camera parameter, the parallax, and the outline of the target.

A processing apparatus includes a collection module and a training module, the training module includes a backbone network and a region proposal network (RPN) layer, the backbone network is connected to the RPN layer, and the RPN layer includes a class activation map (CAM) unit. The collection module is configured to obtain an image, where the image includes an image with an instance-level label and an image with an image-level label. The backbone network is used to output a feature map of the image based on the image obtained by the collection module.

A processing apparatus includes a collection module and a training module, the training module includes a backbone network and a region proposal network (RPN) layer, the backbone network is connected to the RPN layer, and the RPN layer includes a class activation map (CAM) unit. The collection module is configured to obtain an image, where the image includes an image with an instance-level label and an image with an image-level label. The backbone network is used to output a feature map of the image based on the image obtained by the collection module.

The present application relates to the technical field of image recognition and provides a pinch gesture detection and recognition method, which is applied to an electronic device and includes: acquiring, in real time, image data of each frame in a video to be detected; performing a hand location detection on the image data based on a pre-trained hand detection model, to determine a hand position of the image data; performing a skeleton point recognition at the hand position based on the pre-trained skeleton point recognition model, to determine a preset number of skeleton points at the hand position; and determining whether a hand corresponding to the image data is in a pinch gesture or not according to information of a distance between the skeleton points of preset fingers.

The present application relates to the technical field of image recognition and provides a pinch gesture detection and recognition method, which is applied to an electronic device and includes: acquiring, in real time, image data of each frame in a video to be detected; performing a hand location detection on the image data based on a pre-trained hand detection model, to determine a hand position of the image data; performing a skeleton point recognition at the hand position based on the pre-trained skeleton point recognition model, to determine a preset number of skeleton points at the hand position; and determining whether a hand corresponding to the image data is in a pinch gesture or not according to information of a distance between the skeleton points of preset fingers.