Improved YOLOv5's remote sensing image detection algorithm

Addressing the challenges inherent in remote sensing image detection, notably the YOLOv5 detector's subpar accuracy due to limited detection target features, intricate detection backgrounds, and predominantly small targets, this paper presents the RS-YOLOv5 algorithm, an enhancement of the YOLOv5s model. Primarily, the conventional convolution in the backbone network is substituted with full-dimensional dynamic convolution to enrich the information gathered across multiple dimensions, thereby enhancing the extraction of target features. Additionally, a decoupled detection head replaces the traditional detection head, refining classification and positioning tasks while strategically leveraging features from different levels to mitigate background interference on small target detection. Next, adding the Normalized Wasserstein distance (NWD) measurement to the loss function is intended to enhance the accuracy of small target detection. The ablation analysis conducted on the DOTA dataset showcases a noteworthy improvement in the average accuracy of object detection in remote sensing imagery facilitated by the proposed RS-YOLOv5 algorithm, culminating in a 2.5% increase in average accuracy compared to the original algorithm.