Deformable convolution-based motion target detection algorithm
Yijun Tang, Hong Fan, Ruyi Sun, Yi Ping Yang, Shuyu Yu · 2023
For motion target detection in dynamic backgrounds, most traditional methods have drawbacks, such as long computation time or high limitation on the background. This paper proposes a deformable convolution-based motion target detection algorithm by replacing part of the C3 module in the YOLOv5 feature extraction layer with deformable convolution (DCNv3) to introduce long distance dependence and adaptive spatial aggregation, and adding an ECA attention mechanism to reduce the effect of background variation. The the accuracy, recall and mAP of the improved YOLOv5s algorithm increases by 1.6%, 3.8% and 2.3% respectively, and is able to identify moving targets in dynamic backgrounds more accurately than the original algorithm.