A Real-time Object Detection Solution and Its Application in Transportation

Object Detection Algorithms is widely used in transportation. With YOLOv3 however, it is impossible to achieve real-time detection. This paper made some adjustments to YOLOv3, and proposed a new light-scale model named MobileNetv1_yolov3lite. In our MobileNetv1_yolov3lite, we use MobileNetv1 instead of Darknet53 as our backbone network, and we use a newly proposed module yolov3lite for feature fusion. These adjustments achieve significant increases in detecting speed, and can achieve real-time detection. However, it suffers from accuracy loss. In order to improve detecting accuracy, we further modify loss function as well as training methods, which contributes to a higher accuracy.