ESGN-YOLO: Enhancing Multi-Scale Small Object Detection via Efficient Feature Fusion and Adaptive Spatial Modeling

Object detection is crucial in remote sensing, surveillance, and autonomous driving. Detecting small objects remains challenging due to limited pixels, redundant backgrounds, and noise from viewpoint and illumination variations. To address these, we propose ESGN-YOLO, a lightweight model with three improvements. The Efficient Feature Fusion Module (EFFM) enhances multi-scale and directional feature extraction. The Shift-Wise Convolution (SWC) Bottleneck refines fine-grained features and suppresses background redundancy. The Group Normalisation Scale Head (GNSH) further improves detection accuracy and efficiency. Experiments on VisDrone2019 and RS STOD show ESGN-YOLO achieves superior mAP@0.5 (34.5% and 76%) with a compact size (3.7M parameters) and moderate computational cost (12.3 GFLOPs). Fast inference confirms its practicality for real-time UAV deployment and small-object detection under resource-constrained conditions.