Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images

The objects in optical high-resolution remote sensing images (HRRSIs) are usually tiny, dense, and exist in complex backgrounds, which brings great challenges to accurate object detection. This article presents an improved YOLOv5s network-based technique for remote sensing object recognition to overcome these difficulties. First, unnecessary residual modules are pruned from the cross-stage partial layer of conventional YOLOv5s and a refined residual coordinate attention module is incorporated to enhance the feature representation of the densely packed small objects in HRRSIs by introducing the residual structure and the mix pooling operation instead of the existing average pooling. Second, since various scales of objects are present in HRRSIs, the algorithm of differential evolution is adopted to replace the traditional K-means for generating a variety of anchor boxes in different sizes. Third, we replace the commonly used complete intersection over union (IoU) loss function in YOLOv5s with the AW-IoU loss function based on both alpha-IoU and wise-IoU. AW-IoU could expedite bounding box regression and focus more on regular anchor boxes. Finally, instead of nonmaximum suppression (NMS), the SCYLLA (S-IoU) soft-NMS is employed to eliminate the redundant duplicate boxes to detect the dense objects in remote sensing images. Experimental results on the NWPU VHR-10 dataset demonstrate that the proposed YOLOv5s method performs well compared with state-of-the-art algorithms.