Hon Hai Precision Industry Co., Ltd.

Institute for Information Industry

Chieh Lee

Chin-Pin Kuo

Chih-Te Lu

Yung Hsiang Cheng

Jung-Yi Lin

Shih-chao Chien

Chin-Fa Huang

Hsiao-Hsien Chang

Meng-Hsuan Chung

Chin Yi Lai

Chao-Shun Yeh

Chia-Heng Tuan Mu

Fang Chi Huang

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

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.

Method for training an image depth recognition model, method for recognizing image depth, and electronic device

A lane line detection method applied to a vehicle-mounted device is provided. In the method, the vehicle-mounted device converts a foreground image into a bird's-eye view (BEV) image, sets a peak of a horizontal histogram of the BEV image as a starting point for moving the sliding window, and generate a main lane line by fitting multiple non-zero pixels of the horizontal sliding window. The vehicle-mounted device calculates a confidence coefficient of each horizontal sliding window; and sets a previous one before the preset number of continuous horizontal sliding windows as a previous horizontal sliding window and determines an end point of the main lane line according to the previous horizontal sliding window when the confidence coefficients of preset number of continuous horizontal sliding windows are less than a predetermined threshold. The method improves accuracy of identifying lane lines.

Lane line detection method, vehicle-mounted device and storage medium

An image depth recognition method held in a storage medium and running in a disclosed electronic device acquires images to be recognized and two original images. An original image is recognized through a depth recognition network to obtain an initial depth image, and a pose absolute value matrix is generated based on the two processed original images and a pose network, the pose network and the initial depth image generating an initial projection image. The processed two original images are recognized according to the pose absolute value matrix and the preset threshold matrix and adjusted based on errors between the initial depth image, the target image, and the target projection image. The depth recognition network obtains a depth recognition model, and the depth information of the image can be recognized. The method can improve the accuracy of the depth recognition of the image.

Image depth recognition method, electronic device, and storage medium

A method for detecting three-dimensional (3D) objects in relation to autonomous driving is applied in an electronic device. The device obtains detection images and depth images, =inputs the detection images into a trained object detection model to determine categories of objects in the detection images and two-dimensional (2D) bounding boxes of the objects. The device determines object models of the objects and 3D bounding boxes of the object models according to the object categories, and calculates point cloud data of the objects selected and distances from the depth camera to the object models. The device determines angles of rotation of the object models of the objects according to the object models of the objects and the point cloud data, and can determine respective positions of the objects in 3D space according to the distance from the depth camera to the object models, the rotation angles, and the 3D bounding boxes.

Method for detecting three-dimensional objects in relation to autonomous driving and electronic device

A method for assisting safer riving applied in a vehicle-mounted electronic device obtains RGB images of a scene in front of a vehicle when engine is running, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images to three-dimensional (3D) point cloud maps. A curvature of the driving path of the vehicle is calculated, and 3D regions of interest of the vehicle are extracted from the 3D point cloud maps according to a size of the vehicle and the curvature or deviation from straight ahead. The 3D regions of interest are analyzed for presence of obstacles. When no obstacles are present, the vehicle is controlled to continue driving, when presence of at least one obstacle is determined, an alarm is issued.

Method for assisting safer driving and electronic device

A method for obtaining depth images for improved driving safety applied in an electronic device processes first and immediately-following second images which have been captured and processes each to obtain two sets of predicted depth maps. The electronic device determines a transformation matrix of a camera between first and second images and converts the first predicted depth maps into first point cloud maps, and second predicted depth maps into second point cloud maps. The first point cloud maps are converted into third point cloud maps, and the second point cloud maps into fourth point cloud maps. First and fourth point cloud maps are matched and first and second error values are calculated, thereby obtaining a target deep learning network model. Images to be detected are input into the target deep learning network model and depth images are obtained.

Method for obtaining depth images for improved driving safety and electronic device

An image recognition method applied to an electronic device is provided. The method includes constructing a first semantic segmentation network. In response that an initial labeled result of one of a plurality of initial labeled images does not match a preset labeled result, a target image corresponding to the one of the plurality of initial labeled images and a target labeled result of the target image are obtained. A second semantic segmentation network is obtained by training the first semantic segmentation network based on a plurality of the target images and the target labeled result of each target image, and a labeled result of an image to be recognized is obtained by inputting the image to be recognized into the second semantic segmentation network.

Image recognition method, electronic device and readable storage medium

A method for detection of three-dimensional (3D) objects on or around a roadway by machine learning, applied in an electronic device, obtains images of road, inputs the images into a trained object detection model, and determines categories of objects in the images, two-dimensional (2D) bounding boxes of the objects, and parallax (rotation) angles of the objects. The electronic device determines object models and 3D bounding boxes of the object models and determines distance from the camera to the object models according to size of the 2D bounding boxes, image information of the detection images, and focal length of the camera. The positions of the object models in a 3D space can be determined according to the rotation angles, the distance, and the 3D bounding boxes, and the positions of the object models are taken as the position of the objects in the 3D space.

Method for detection of three-dimensional objects and electronic device

An assistance method of safe driving applied in a vehicle-mounted electronic device obtains RGB images of scene in front of a vehicle, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images into three-dimensional (3D) point cloud maps, determines 3D regions of interest therein, and obtains position and size information of objects in the 3D regions of interest. When the position information satisfies a first preset condition and/or the size information satisfies a second preset condition, the presence of obstacles in the 3D regions of interest is determined and controls the vehicle to issue an alarm. When the position information does not satisfy the first preset condition and/or the size information does not satisfy the second preset condition, the 3D regions of interest are determined as obstacle-free, and permitting the vehicle to continue driving.

Assistance method of safe driving and electronic device

A method for detecting three-dimensional (3D) objects in roadway is applied in an electronic device. The device inputs training images into a semantic segmentation model, and performs convolution operations and pooling operations on the training images and obtains feature maps. The electronic device performs up-sampling operations on the feature maps to obtain first images, classifies pixels on the first images, calculates and optimizes a classification loss and obtains a trained semantic segmentation model. The device inputs the detection images into the trained semantic segmentation model, determines object models of the objects, point cloud data and distances from the depth camera to the object models, determines rotation angles of the object models according to the point cloud data and the object models, and determines positions of the object models in 3D space according to the distances, the rotation angles, and positions of the objects.

Method for detecting three-dimensional objects in roadway and electronic device

A method for adjusting point cloud density, an electronic device, and a storage medium are provided. In the method an initial point cloud map and a distance determination threshold of a robot are obtained. A plurality of target regions in the initial point cloud map are determined, and an environmental complexity value of each target region is calculated. The initial point cloud map is divided into submaps, and a point cloud density coefficient of each submap is determined. The initial point cloud map is adjusted according to the point cloud density coefficient and the target point cloud map is obtained. By utilizing such method, adjustment efficiency and an accuracy of point cloud density can be improved.

Method for adjusting point cloud density, electronic device, and storage medium

An attack path detection method, attack path detection system and non-transitory computer-readable medium are provided in this disclosure. The attack path detection method includes the following operations: establishing a connecting relationship among a plurality of hosts according to a host log set to generate a host association graph; labeling at least one host with an abnormal condition on the host association graph; calculating a risk value corresponding to each of the plurality of hosts; in a host without the abnormal condition, determining whether the risk value corresponding to the host without the abnormal condition is greater than a first threshold, and utilizing a host with the risk value greater than the first threshold as a high-risk host; and searching at least one host attach path from the high-risk host and the at least one host with the abnormal condition according to the connecting relationship of the host association graph.

Attack path detection method, attack path detection system and non-transitory computer-readable medium

A shielding cage assembly () for accommodating transceiver modules includes a body cage (), a cover cage (), and dividing walls (). The body cage includes a bottom wall () with front cutouts (), and two side walls (). The cover cage includes a top wall () with front cutouts (). Each dividing wall has top and bottom barbs () at a front end thereof. In assembly, the barbs are engaged in the corresponding cutouts. The bottom wall, side walls, dividing walls and cover cage cooperatively define cavities therebetween for receiving the transceiver modules. The mechanical strength of the shielding cage assembly at front openings of the cavities is enhanced by the barbs of the dividing walls being engaged in the cutouts of the body cage and the cover cage. The shielding cage assembly can therefore withstand repeated insertion and removal of the transceiver modules without sustaining expansion or distortion.

Shielding cage assembly with reinforcing dividing walls

A hinge device comprises a male hinge member and a female hinge member. The male hinge member includes a pivoting rod at an end thereof, and a first mounting plate at an opposite end thereof, the first mounting plate being used for fixing the male hinge member onto a display unit of a portable computer. The female hinge member includes a body and a second mounting plate for fixing the female hinge member onto a mainframe unit of the portable computer. A first contacting section is defined on an outer periphery of the pivoting rod of the male hinge member. A second contacting section is defined on an inner periphery of the body for contacting with the first contacting section. A pair of grooves are defined in the second contacting section for retaining lubrication grease. The second mounting plate defines a receiving slot adjacent to the second contacting section for spacing the second contacting section from the second mounting plate to obtain a uniform torque resistance during rotation movements of the second contacting section in different directions.

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Taipei, Taiwan

Chieh Lee