Year: 2026
Type: article
Abstract: 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 [email protected] 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 [email protected] reaching 0.998, surpassing YOLOv5s (0.994) and YOLOv8s (0.988), and slightly exceeding YOLOv11s (0.989). Although [email protected]: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.
Authors: Seongjung Na, Seunghyun Gwak, Suhwan Kim
Institutions: Korea National Defense University, Seoul National University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7128375267