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 introduced to the multi-scale and global context features of the network's abstraction layer, so as to supplement information 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 representing 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 proposed method is competent in the task of moving vehicle detection. [ABSTRACT FROM AUTHOR]