Research on Driving Target Detection Algorithm Based on Improved Centernet
In multi-target detection,when the target moves,it can cause changes in its shape or occlusion,which af-fects the accuracy of target detection.Therefore,to address the complex driving environment in the field of au-tonomous driving and the dependence of traditional object detection algorithms on a large number of pre-set prior boxes,poor generalization ability,and low detection accuracy,an improved CenterNet object detection algorithm is pro-posed.DLA-34 is selected as the backbone feature extraction network,and a lightweight module ECA-Net that can adaptively determine convolution kernels and interact across channels is introduced to achieve CenterNet improvement.The experimental results on the kitti dataset showed that the improved CenterNet improved the AP of the original network by 1.39％in the car category,11.16％in the pedestrian category,and 19.45％in the cyclist category.Com-pared with the Yolov3 network,the improved CenterNet also showed significant improvements in object detection ac-curacy in different categories.
万方数据
维普
