ICGM-YOLO: UAV remote sensing small target detection algorithm based on feature fusion enhancement module and ghost convolution

Remote sensing images captured at high altitudes are affected by background noise and low resolution, leading to suboptimal performance of commonly used small object detection algorithms. This paper introduces ICGM-YOLO, an enhanced approach designed to improve the precision of detecting small objects within remote sensing images. The methodology refines YOLOv8 for optimal performance in this context. First, within the feature extraction network, iterative attention feature fusion combines feature maps and integrates features from different layers. Second, the convolutional layer and C2f module are replaced with the GhostConv module and C3Ghost to extract features. Finally, this paper replaces CIoU with the MFIoU loss function, which combines the new bounding box MPDIoU based on minimum point distance with Focaler-IoU. This replacement accelerates model convergence and enhances detection recall. Experimental results from the Visdrone2019 dataset indicate that ICGM-YOLO outperforms the original YOLOv8 by achieving an 8.1% improvement in mAP0.5 detection rate and a 5.3% increase in mAP0.5:0.95 detection rate. Additionally, ICGM-YOLO reduces the parameter count by 48.2% and decreases computational complexity by 45.2%.