Research on dense object detection methods in congested environments of urban streets and roads based on DCYOLO

Abstract

The urban street is a congested environment that contains a large number of occluded and size-differentiated objects. Aiming at the problems of the loss of the target to be detected and low detection accuracy resulting from this situation, a newly improved algorithm, based on YOLOv4, DCYOLO is proposed. Firstly, a Difference sensitive network (DSN) is introduced to extract the edge features of objects from the original image. Then, assign the edge features back to increase the edge intensity of the object in the original image and ultimately improve the detection performance. Secondly, the feature fusion module (CFFB) based on context information is introduced to realize the cross-scale fusion of shallow fine-grained features and deep-level features, to strengthen the cross-scale semantic information fusion of feature maps and eventually improve the performance of object detection. At last, in the network prediction part, the SIOU loss function replaces the original CIOU loss function to improve the convergence speed and accuracy of object detection. The experiments based on MS COCO 2017 and self-made datasets show that, compared with the YOLOv4, the detection accuracy of DCYOLO models is greatly improved with an increase of 9.1 percentage points in AP and 10.4 percentage points in AP_s. Compared with YOLOv5x and Faster R-CNN, DCYOLO shows higher accuracy and better detection performance. The experiment result proves that the DCYOLO algorithm can adapt to the dense object detection requirements in the congested environment of urban streets.