Interframe target regression network for vehicle detection in UAV video

UAV video has many advantages of flexible view, continuous view and wide monitoring scope, and at the same time, there are many problems, such as crowded targets, strong motion noises and so on, which make target detection difficult. To solve these problems, this paper proposes a video vehicle detection algorithm based on the interframe target regression network. According to the characteristics of crowded vehicles in UAV video, soft non maximum suppression is proposed as the detecting-box merging strategy of FCOS, and thus a single-frame vehicle detector is constructed. In order to deal with the problem that the single-frame detector can be easily disturbed by motion noise when it is directly applied to video detection, thus resulting in the change of the confidence level for the same target, an interframe target regression network is designed. The target features of adjacent multiple frames are fused by using interframe movement continuity, and the fused features are matched with the target features of the current frame to output the prediction results. Finally, the detection performance is improved by correcting prediction results through single-frame detection results. Compared with FCOS and FGFA, the average precision of the proposed algorithm is improved by 2% and 5% respectively, reaching 47.42%.Experimental results show that it is better than the existing FCOS and FGFA, and has better robustness and generalization. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.