A real-time vehicle detection algorithm based on instance segmentation

Vehicle detection has achieved great progress in intelligent transport system, which has the advantages of low risk and high accuracy. In this work, we adopt one-stage algorithm YOLACT to train our model on dataset MS COCO 2017. We propose (Precise) P-CIoU loss to better predict bounding box regression in vehicle detection. In terms of the final candidate bounding box selection, this paper proposes skip-calculation NMS mechanism, in which IoU threshold γ and normalized central point distance method incorporated. This mechanism can effectively solve similar occlusion problem. The experimental results show that our approach achieves performance improvement with gains about 2%, without sacrificing inference efficiency.