Hon Hai Precision Industry Co., Ltd.

Yu-hsuan Chien

Chin-Pin Kuo

A method of detecting moving objects is provided. The method obtains point cloud data set of a target scene and an image of the target scene. The method detects one or more stationary object areas from the image. The method records point cloud data, which corresponds to the stationary object areas in the point cloud data set, to be first point cloud data. The method determines a velocity range of the first point cloud data according to the first point cloud data. The method further determines whether one or more moving objects are present in the target scene according to second point cloud data and the velocity range of the first point cloud data. The second point cloud data is the point cloud data of the point cloud data set excluding the first point cloud data. A related electronic device and a non-transitory storage medium are provided.

Method of detecting moving objects, device, electronic device, and storage medium

A method for collision warning implemented in an electronic device includes fusing obtained radar information and image information; recognizing at least one obstacle in a traveling direction of a vehicle according to the fused radar information and image information; determining motion parameters of the at least one obstacle and the vehicle according to the radar information and the image information; and calculating a collision time between the vehicle and the at least one obstacle according to the motion parameters, and issuing a collision warning.

Method for collision warning, electronic device, and storage medium

A lane line recognition method applied to an electronic device is provided. In the method, the electronic device converts a first foreground image into a bird's-eye view (BEV) image and determines the initial positions of the left lane line and the right lane line of the BEV image and fits a first curve and a second curve after a first sliding window slid each time and fits a third curve and a fourth curve after a second sliding window slid each time. The electronic device recognizes the left lane line according to the second curve and the right lane line according to the fourth curve. The method improves accuracy for recognizing lane lines of a road when a vehicle is moving on the road.

Lane line recognition method, electronic device and storage medium

A method for estimating depth implemented in an electronic device includes obtaining a first image; inputting the first image into a depth estimation model, and obtaining a first depth image; obtaining a depth ratio factor, the depth ratio factor indicating a relationship between a relative depth and a depth of each pixel in the first depth image; and obtaining depth information of the first depth image according to the first depth image and the depth ratio factor.

Method for estimating depth, electronic device, and storage medium

An image detection obtains original image. The original image is corrected to obtain a corrected image. Median filtering is performed on the corrected image to obtain a filtered image. A contrast of the filtered image is adjusted to obtain an adjusted image. Bilateral filtering is performed on the adjusted image to obtain an enhanced image. Defects in the enhanced image is detected. The method can detect defects in images accurately and efficiently.

Image detection method, computing device, and storage medium

A method for training a depth estimation model implemented in an electronic device includes obtaining a first image pair from a training data set; inputting the first left image into the depth estimation model, and obtaining a disparity map; adding the first left image and the disparity map, and obtaining a second right image; calculating a mean square error and cosine similarity of pixel values of all corresponding pixels in the first right image and the second right image; calculating mean values of the mean square error and the cosine similarity, and obtaining a first mean value of the mean square error and a second mean value of the cosine similarity; adding the first mean value and the second mean value, and obtaining a loss value of the depth estimation model; and iteratively training the depth estimation model according to the loss value.

Method for training depth estimation model, method for estimating depth, and electronic device

A method for improving the reconstruction of images obtains an object image and a reference image and extracts a first edge of the object image and a second edge of the reference image based on a predetermined algorithm. A first vector of the plurality of first pixels to the first edge and a second vector of the plurality of second pixels to the second edge are obtained and a determination made as to whether the first vector and the second vector are consistent. A loss between the first vector and the second vector is calculated if the first and second vector are not consistent and a predetermined model is corrected based on the loss, the reference image being reconstructed into the object image based on corrected predetermined model. An image reconstruction device and a non-transitory storage medium are also disclosed.

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Taipei, Taiwan

Yu-hsuan Chien