Road Small Target Detection Network Based on Feedback Mechanism and Dilated Convolution

Abstract

With the development of Convolutional Neural Network(CNN) and feature pyramids, target detection has made breakthroughs in large and medium targets, but there are missed detections and low detection accuracies for small targets.Aiming at the reasons for less information of small targets in the picture and the difference in the size of small targets from that of large targets, this study proposes the YOLOv4-RF algorithm based on the YOLOv4 algorithm and further enhances the detection performance of the model for small targets.This study uses dilated convolution to replace the pooled pyramid of the neck in YOLOv4 to reduce semantic loss and obtain a larger receptive field in the deeper part of the network.Moreover, the backbone network is lightweight and a feedback mechanism from the feature pyramid to the backbone network is added.The features from shallow and deep fusion are processed again, which retains more feature information of small targets and improves the effectiveness of the network classification and positioning.Finally, because the small target object belongs to the difficult detection sample, the focal loss function is introduced to increase the weight loss of the difficult sample and form the YOLOv4-RF algorithm.The experimental data on the KITTI dataset show that the detection accuracy of YOLOv4-RF in each category is higher than that of YOLOv4, and the Mean Average Precision([email protected]) is improved by 1.4% by reducing the model by 138 MB.