Study on Multi-Scale Feature and Dual-Source Motion Perception for Vehicle Detection
[Objective]Vehicle detection is critical for urban intelligent transportation.Focusing on small target problems,high-density problems,and motion attribute problems,this study takes traffic surveillance images as input and aims to detect moving vehicles.[Method]Based on the anchor-free CenteNet,a detection method of multi-scale features and dual-source motion perception was proposed.Firstly,coordinate attention was intro-duced to the multi-scale and global context features of the network's abstraction layer,so as to supplement infor-mation in multiple stages and at multiple levels and improve the model's understanding of vehicles and scenes.Secondly,through fuzzy textures representing actual motion features of vehicles and optical flow knowledge rep-resenting general motion features of vehicles,the model's perception ability of moving vehicles was constructed.[Result]The experimental data came from the public dataset UA-DETRAC.The mean average precision(mAP)and frames per second(FPS)were used as the evaluation metrics of accuracy and speed.Experiment results show that the mAP and FPS of the proposed method are 70%and 30 frame/s respectively,which have the best balance between speed and accuracy among other compared methods.[Conclusion]It maintains that the pro-posed method is competent in the task of moving vehicle detection.
万方数据
维普
