In order to solve the problems of low accuracy and slow speed of target detection in video images, an improved YOLOV3-based object detection method is proposed. By introducing GIOU Loss, the non-overlapping part problem that the original IOU cannot directly optimize can be solved. After drawing on the idea of densely connected networks, the three residual blocks in YOLOV3 are replaced with three dense blocks, and the denseness is combined with Max Pooling to strengthen the denseness. After transferring the features between the connected blocks, and replacing the IOU and the original network to detect the connection structure, a new network structure is designed. The number of parameters is reduced, the feature reuse and fusion are enhanced, and the effect is better than the original method. The experimental results show that comparing with the original algorithm, the improved GDT-YOLOV3 algorithm has excellent results in both simple and complex traffic scenarios. The average detection accuracy of the algorithm proposed in this paper is up to 92.77%, and the speed has reached 25.3 f/s, which basically meets the real-time performance. In addition, in terms of detection accuracy, the improved GDT-YOLOV3 performs better than SSD512, YOLOV2, and YOLOV3.