DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection

Small objects detection is a challenging task in computer vision due to the limited semantic information that can be extracted and the susceptibility to background interference. In this paper, we propose a decoupled multi-scale small object detection algorithm named DMS-YOLOv5. The algorithm incorporates a receptive field module into the feature extraction network for better focus on low-resolution small objects. The coordinate attention mechanism, which combines spatial and channel attention information, is introduced to reduce interference from background information and enhance the network's attention to object information. A detection layer tailored to small-sized objects is added to compensate for the loss of small object information in multiple downsampling operations, greatly improving the detection capability of small objects. Next, The decoupled network is introduced into the detection head network for branch processing of classification and bounding box regression tasks. Finally, the bounding box loss function is improved to alleviate missed detection problems caused by the concentration of small objects and mutual occlusion between objects. The improved method achieved a mean average precision improvement of 12.1% on VisDrone2019-DET dataset compared to the original method. In comparison experiments with similar methods, our proposed method also demonstrated good performance, validating its effectiveness.