Hon Hai Precision Industry had 64 patents in artificial intelligence during Q4 2023. The patents filed by Hon Hai Precision Industry Co Ltd in Q4 2023 focus on methods for safer driving assistance and product defect detection using electronic devices. These methods involve obtaining RGB images, processing them with depth estimation models, converting them to 3D point cloud maps, and analyzing regions of interest for obstacles. Alarm is issued when obstacles are detected during driving assistance, and defect detection involves training autoencoder and defect detection models. The patents also include methods for managing autonomous driving by assessing distances to objects in the vehicle’s path and issuing alarms when obstacles are present. GlobalData’s report on Hon Hai Precision Industry gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Hon Hai Precision Industry grant share with artificial intelligence as a theme is 12% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Method for assisting safer driving and electronic device (Patent ID: US20230415760A1)
The patent filed by Hon Hai Precision Industry Co Ltd describes a method for assisting safer driving using a vehicle-mounted electronic device. The method involves obtaining RGB images of the scene in front of the vehicle, processing them with a trained depth estimation model to create depth images, converting these depth images into 3D point cloud maps, calculating the curvature of the driving path, and extracting 3D regions of interest based on the vehicle size and curvature. These regions are then analyzed for obstacles, and if any are detected, an alarm is issued to the driver.
Furthermore, the method includes determining the driving state of the vehicle based on the steering wheel angle, calculating the curvature for straight or turning driving states, and analyzing the size and direction of deviation of the 3D regions of interest. The depth estimation model used in the process comprises convolutional neural networks for depth and pose estimation, which are trained to reconstruct images and minimize loss values. The electronic device implementing this method includes a processor and storage medium storing instructions for obtaining RGB images, processing them with the depth estimation model, calculating curvature, determining regions of interest, and issuing alarms when obstacles are detected. The device also includes instructions for training the depth estimation model and converting depth images into 3D point cloud maps.
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