Hon Hai Precision Industry Co., Ltd.

Chin-Pin Kuo

Chih-Te Lu

Tzu-Chen Lin

Tung-Tso Tsai

Jung-Hao Yang

Wan-Jhen Lee

Shih-chao Chien

Guo-Chin Sun

Chieh Lee

I-Hua Chen

Yu-hsuan Chien

Yueh Chang

Jung-Yi Lin

Chih-Yuan Chuang

Wei-chun Wang

Chung-yu Wu

Tsung-wei Liu

Tsung-Yuan Tu

Chien-Tsung Lee

Yu-kai Zhou

Young-Way Liu

Yu-Cheng Chen

Shuo-Yen Chung

Chun-Chang Chang

A crowding degree estimation method applied to an electronic device is provided. In the method, the electronic device determines a remaining carrying space of a carriage according to an image viewed from a top of an interior of the carriage, and determines a recommended remaining number of people that can be carried according to the remaining carrying space, the number of people already in the carriage, and a maximum number of people that can be carried of the carriage. The electronic device determines the crowding degree according to the recommended remaining number of people. The method can determine the crowding degree of each carriage of a public transportation and recommend passengers with less crowded carriages to save passengers' waiting time.

Crowding degree estimation method, electronic device and storage medium

A virtual scene generation method applied to an electronic device is provided. The electronic device identifies object prediction information of each real object in each of real scene images. A first virtual image corresponding to each real scene image is obtained according to the object prediction information of each real object. A second virtual image and a texture difference image corresponding to each real scene image are generated. A target image corresponding to each real scene image is generated according to the second virtual image and the texture difference image corresponding to each real scene image. Once a virtual scene generation model is generated based on the real scene images, the first virtual image, the second virtual image, the target image corresponding to each real scene image, a virtual scene corresponding to an image is obtained using the virtual scene generation model and the virtual scene simulator.

Virtual scene generation method, electronic device and storage medium

A method for processing images to remove distortions of various types without requiring retraining for per-type processing models, applied in an electronic device, establishes a distortion removal model, and inputs distorted images in the distortion removal model. General distortions are removed by the distortion removal model and the restored images are input into a deep learning model for final image processing. The deep learning model of the present disclosure finally corrects distortion in the images so that distortion-free images are obtained.

Method in image processing for removal of general distortions and electronic device

A method acquires a first image and a second image of a target object being inputted into the depth estimation network for outputting a depth image. A pixel posture conversion relationship between the first image and the second image is obtained. The pixel posture conversion relationship includes a position relationship between each first pixel in the first image and a second pixel in the second image, which correspond to a same part of the target object. A restored image is generated based on the depth image, the pixel posture conversion relationship, and pre-obtained camera parameters. A loss of the depth estimation network is determined based on a difference between the first image, the depth image, the restored image, and the second image for adjusting the parameters of the depth estimation network. A training apparatus, and an electronic device applying the method are also disclosed.

Method for training depth estimation model, training apparatus, and electronic device applying the method

A method of determining a congestion degree of a compartment applied to an electronic device is provided. The electronic device determines a remaining carrying space of the compartment according to a binarized passenger distribution image of the compartment. A predicted remaining number of passengers that can be carried in the compartment is determined according to the remaining carrying space. The electronic device determines a recommended remaining number of passengers that can be carried in the compartment based on a number of passengers currently in the compartment, the predicted remaining number of passengers, and a maximum number of passengers that can be carried in the compartment. The electronic device can determine the degree of congestion of the compartment according to the recommended remaining number of passengers that can be carried in the compartment and the maximum number of passengers that can be carried in the compartment.

Method of determining degree of congestion of compartment, electronic device and storage medium

An image processing method and an electronic device are disclosed, the method acquires training data in response to receiving an image processing instruction and trains a deep learning model with the training data by using a preset deep learning framework to obtain an initial model. A data type of the initial model is converted to increase the data processing speed of the model. A correction layer is added to the converted initial model and the initial model is optimized by training weights of the correction layer to obtain an image processing model to further increase the data processing speed. After acquiring an image from the image processing instruction, the image processing model can be used to process the image and an image processing result can be outputted. The image is processed based on the optimized image processing model, and the data processing speed and accuracy are guaranteed.

Image processing method, electronic device and storage medium

A method for controlling vehicles, an electronic device and a storage medium are provided. In the method, a license plate image of a target vehicle in front of a vehicle to be controlled and point cloud data of the target vehicle through a radar are acquired. The license plate image is identified to obtain initial vehicle data of the target vehicle. Target vehicle data of the target vehicle is calculated based on preset ranges, vehicle data of the vehicle to be controlled, the point cloud data and the initial vehicle data. In response that the target vehicle data meets preset conditions, the vehicle data of the vehicle to be controlled is adjusted according to the target vehicle data. The method can improve an accuracy of vehicle detection and a driving safety.

Method for controlling vehicles, electronic device, and storage medium

A lane line labeling method applied to an electronic device is provided. In the method, the electronic device acquires a target image corresponding to a target lane. Motion trajectory points of a target vehicle driving on the target lane are obtained. The electronic device determines projected pixel coordinates of the motion trajectory points on the target image, and determines target pixel coordinates corresponding to target lane lines on the target lane based on the projected pixel coordinates. Once target camera coordinates corresponding to the target pixel coordinates are obtained, the electronic device labels the target lane lines according to the target camera coordinates.

Lane line labeling method, electronic device and storage medium

A method for segmenting roads in images implemented in an electronic device includes obtaining a road image; obtaining a plurality of regions of interest based on the road image; generating a splicing region based on the plurality of regions of interest; and inputting the splicing region into a pre-trained road segmentation model, and obtaining a road segmentation image and a segmentation result in the road segmentation image.

Method for segmenting roads in images, electronic device, and storage medium

A method for training an autoencoder implemented in an electronic device includes obtaining a stereoscopic image as the vehicle is in motion, the stereoscopic image includes a left image and a right image; generating a stereo disparity map according to the left image; generating a predicted right image according to the left image and the stereo disparity map; and calculating a first mean square error between the predicted right image and the right image.

Method for training autoencoder, electronic device, and storage medium

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 method for training a depth recognition model implemented in an electronic device includes determining test objects from test images, and obtaining a first image and a second image; calculating a test projection slope of each test object according to coordinates of each pixel point of each test object in the test images; generating a threshold range according to the plurality of test projection slopes; recognizing a type of terrain corresponding to a position of each initial object; adjusting an initial ground area in the first image, and obtaining a target ground area in the first image; generating a target height loss of a preset depth recognition network, an initial depth image corresponding to the first image, and the target ground area; and adjusting the preset depth recognition network according to the target height loss and a depth loss, and obtaining a depth recognition model for recognizing depth of images.

Method for training depth recognition model, electronic device, and storage medium

A method for processing images implemented in an electronic device includes obtaining images during moving of a vehicle; obtaining instance segmentation images by segmenting the images; obtaining a predicted disparity map by reconstructing the left images based on a pre-established autoencoder; generating a first error value of the autoencoder for the images according to the left image, the predicted disparity map, and the right image, generating a second error value of the autoencoder for the instance segmentation image according to the left image of instance segmentation, the predicted disparity map, and the right image of instance segmentation; establishing an autoencoder model by adjusting the autoencoder according to the first error value and the second error value; obtaining a test image as the vehicle is moving, and obtaining a target disparity map; and obtaining a depth image corresponding to the test image by converting the target disparity map.

Method for processing images, electronic device, and storage medium

The present application relates to image processing and provides a method for training an image depth recognition model, a method for recognizing image depth, and an electronic device. The method obtains static objects, dynamic objects, a dynamic position by performing an instance segmentation on the first image and the second image. A target dynamic object and a feature dynamic object are selected from the dynamic objects and the dynamic objects. A target image and a target projection image are generated according to the target dynamic object and the feature dynamic object. A depth recognition model is trained based on the target image, and the target projection image. The to-be-recognized image is recognized by the depth recognition model.

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Taipei, Taiwan

Chin-Pin Kuo