Improving Small Object Detection Performance by Enhancing the YOLO Model

Accurate detection of small objects is critical in military surveillance and drone monitoring, yet standard detectors often fail due to feature loss during down-sampling. This study proposes a lightweight YOLOv5-based model optimized for small-object detection. The design introduces a high-resolution P2 detection head and removes the P5 head, while anchor boxes are re-optimized using a genetic algorithm aligned with dataset statistics. On a drone small-object dataset (24-32 px), the proposed model achieved +0.062 higher mAP@0.5 than YOLOv5s, +0.041 higher than YOLOv8s, and +0.020 higher than YOLOv11s, while maintaining fewer parameters (5.3M vs. 7.0-11.1 M). Additional experiments on medium-sized objects (64-96 px) confirmed strong generalization, with mAP@0.5 reaching 0.998, surpassing YOLOv5s (0.994) and YOLOv8s (0.988), and slightly exceeding YOLOv11s (0.989). Although mAP@0.5:0.95 was marginally lower compared to YOLOv8s and YOLOv11s, the proposed model achieves an effective trade-off between accuracy and efficiency, demonstrating suitability for edge deployment in defense and real-time monitoring applications.