MD-YOLOv8: A Multi-Object Detection Algorithm for Remote Sensing Satellite Images

Abstract

The technology for target recognition in remote sensing satellite images is widely applied in daily life, and research on detecting and recognizing targets in remote sensing images holds significant academic and practical importance. To address the challenges of extreme scale variations, dense target distributions, and low-resolution artefacts in remote sensing images, this paper proposes a new multi-object detection network based on the YOLOv8 architecture—MD-YOLOv8. The main contributions of this paper are threefold: (1) the design of the multi-frequency attention downsampling module, which integrates the ADown module with Haar wavelet transforms and pixel attention; (2) the proposal of the adaptive attention network (DMAA) module, an enhanced multiscale feature extractor based on the multiscale feature extraction attention mechanism; (3) the integration of both modules into the YOLOv8 backbone to achieve superior performance in remote sensing image detection. Based on the DOTA-1.0 dataset for training, experimental results show that the MD-YOLOv8 network achieves improvements in precision, recall rate, and [email protected], reaching 82.69%, 78.28%, and 82.05%, respectively; these represent increases of 3.76%, 3.43%, and 4.37% compared to the original model. In practical image detection, MD-YOLOv8 demonstrates higher recognition quality and can flexibly respond to various target types. The MD-YOLOv8 network effectively meets the accuracy requirements for target detection in remote sensing satellite images.