Remote Sensing Images Detection Algorithm Based on SA-YOLOv5s
Remote sensing images have problems such as unclear target features,complex background information,and low detection efficiency.This article proposes an improved SA-YOLOv5s remote sensing image detection algorithm to address these issues.The Shuffle Atten-tion module was added to the convolutional blocks of the YOLOv5 backbone feature extraction and neck feature fusion network,and experiments were conducted on the publicly available RSOD remote sensing image dataset.The experimental results show that the average accuracy of the improved algorithm is 96.2%,which is 1.1%higher than the original YOLOv5s algorithm.
万方数据
维普
