FYCFNet: Vehicle and Pedestrian Detection Network based on Multi-model Fusion

Abstract

Vision-based solutions for target detection in autonomous driving are very much about the accuracy of detection. A correct or incorrect detection may cause or avoid a traffic accident. Therefore, in this paper, to further improve the detection accuracy of vision schemes, we propose a multi-model fusion network: Fusion Network with YoloV5 and CBNEet Faster-RCNN (FYCFNet) that fuses a one-stage target detection model and a two-stage model, which consists of three parts: the first part is a single-stage YOLOV5 [1] detection model, the second part is a Faster-RCNN [2] with CBNet-V2 [3] as the backbone, and the third part is the post-fusion head of weighted boxes fusion. We tested the performance of this network and compared it with other mainstream networks, and verified that the network achieves a very impressive accuracy improvement.