US Patent Application 17895517. METHOD FOR DETECTING THREE-DIMENSIONAL OBJECTS IN ROADWAY AND ELECTRONIC DEVICE simplified abstract
METHOD FOR DETECTING THREE-DIMENSIONAL OBJECTS IN ROADWAY AND ELECTRONIC DEVICE
Organization Name
HON HAI PRECISION INDUSTRY CO., LTD.
Inventor(s)
CHIN-PIN Kuo of New Taipei (TW)
METHOD FOR DETECTING THREE-DIMENSIONAL OBJECTS IN ROADWAY AND ELECTRONIC DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17895517 titled 'METHOD FOR DETECTING THREE-DIMENSIONAL OBJECTS IN ROADWAY AND ELECTRONIC DEVICE
Simplified Explanation
- The patent application describes a method for detecting 3D objects on roadways using an electronic device. - The device uses a semantic segmentation model to analyze training images and extract features. - Convolution and pooling operations are performed on the training images to obtain feature maps. - The feature maps are then up-sampled to generate first images. - The device classifies pixels on the first images and calculates a classification loss to optimize the model. - The trained semantic segmentation model is then used to analyze detection images. - The device determines object models, point cloud data, and distances from a depth camera to the object models. - Rotation angles of the object models are determined based on the point cloud data and object models. - Positions of the object models in 3D space are determined using the distances, rotation angles, and object positions.
Original Abstract Submitted
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.
