An Improved YOLOv8 Detector for Multi-Scale Target Detection in Remote Sensing Images

Target detection via remote sensing is extensively utilized across diverse domains because of its inherent potential value in applications. However, most objects within remote sensing images consist of multi-scale and dense small objects, observed from diverse angles against complex backgrounds, resulting in insufficient detection performance. To enhance the detection accuracy and robustness in detecting multi-scale objects, we present the YOLO-GE algorithm based on you only look once (YOLO). We introduce the ghost convolution hierarchical graph (G-HG) block that combines ghost convolutions and the cross-stage partial (CSP) strategy. This enhancement can efficiently utilize redundant feature maps, broaden the receptive field, and accurately extract multi-scale objects and advanced semantic features in complex backgrounds. By incorporating the G-HG block, we establish the ghost-convolution enhanced hierarchical graph (GE-HGNet) feature extraction backbone, thereby enhancing its ability to capture multi-scale object features and advanced semantic information. Additionally, we develop the E-SimAM attention mechanism using residual techniques to address the low-resolution feature loss limitation, thereby enhancing the precision in identifying low-resolution features against intricate backgrounds. Furthermore, to improve the capability of detecting densely packed small objects, we reconstruct the structure of the neck and add a tiny detection head. This additional tiny detection head is specifically designed to better focus on densely packed small targets, fully leveraging the fine-grained information in shallow feature maps. Extensive experiments conducted on the DIOR, NWPU VHR-10, and VisDrone2019 datasets demonstrate the effectiveness and robustness of our YOLO-GE algorithm. Notably, compared to the state-of-the-art algorithm, our YOLO-GE-n achieves improvements of 20.1% and 22.2% in mAP0.5 and mAP0.5:0.95 respectively on the VisDrone2019 dataset.