Addressing the critical challenges of weak edge representation, complex multi-scale backgrounds, and inefficient model structures in UAV-based small object detection, this paper proposes YOLO-GML—a lightweight yet powerful detector evolved from YOLOv8n. The model integrates three key innovations: LearnableDoGFilter and GaussianFilter module to enhance target contours and suppress background interference; multi-scale variable kernel convolution (MKConv） module for hierarchical receptive field fusion of local and global contexts; and a lightweight architecture achieved by pruning deep downsampling stages and optimizing the detection Head. Extensive experiments on the VisDrone2019 dataset demonstrate that YOLO-GML not only significantly reduces the parameter count to only 2.044×106 down from 3.008×106 but also achieves superior detection accuracy, with mAP50 rising from 33.633% to 42.818% and mAP50.95 from 19.628% to 25.830%. This work establishes a new leading balance between lightweight design and high precision, offering an efficient and practical solution for real-time UAV vision applications.