关键词: 车辆检测, 特征融合, 卷积神经网络, 实时检测, YOLOv3

Abstract: Vehicle detection on image or video data is an important but challenging task for urban traffic surveillance. The difficulty of this task is to accurately locate and classify relatively small vehicles in complex scenes. In response to these problems, this paper presents a single deep neural network（DF-YOLOv3） for fast detecting vehicles with different types in urban traffic surveillance. DF-YOLOv3 improves the conventional YOLOv3 by first enhancing the residual network to extract vehicle features, then designing 6 different scale convolution feature maps and merging with the corresponding feature maps in the previous residual network, to form the final feature pyramid for performing vehicle prediction. Experimental results on the KITTI dataset demonstrate that the proposed DF-YOLOv3 can achieve efficient detection performance in terms of accuracy and speed. Specifically, for the 512×512 input model, using NVIDIA GTX 1080Ti GPU, DF-YOLOv3 achieves 93.61% mAP（mean average precision） at the speed of 45.48 f/s（frames per second）. Especially, as for accuracy, DF-YOLOv3 performances better than those of Fast R-CNN, Faster R-CNN, DAVE, YOLO, SSD, YOLOv2, YOLOv3 and SINet.

Key words: vehicle detection, feature fusion, convolutional neural network, real-time detection, YOLOv3