Abstract: Aiming at the problems of a large number of small targets in UAV video stream detection, insufficient contextual semantic information due to low image transmission quality, slow inference speed of traditional algorithm fusion features, and poor training effect caused by unbalanced dataset category samples, this paper proposes a vehicle object detection algorithm based on improved Deformable DETR for UAV video streaming. In terms of model structure, this method designs a cross-scale feature fusion module to increase the receptive field and improve the detection ability of small objects, and adopts the squeeze-excitation module for object_query to improve the response value of key objects and reduce the missed or false detection of important objects. In terms of data processing, online difficult sample mining technology is used to improve the problem of uneven distribution of class samples in the data set. The experimental results show that the improved algorithm improves the average detection accuracy by 1.5% and the small target detection accuracy by 1.2% compared with the baseline algorithm without detection speed degradation.

Key words: Deformable DETR, object detection, cross-scale feature fusion module, object query squeeze-and-excitation, online hard sample mining