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

Abstract

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.