A Comprehensive Literature Review on YOLO-Based Small Object Detection: Methods, Challenges, and Future Trends

Small object detection has been a focus of attention since the emergence of deep learning-based object detection. Although classical object detection frameworks have made significant contributions to the development of object detection, there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes. In particular, the YOLO (You Only Look Once) series of detection models, renowned for their real-time performance, have undergone numerous adaptations aimed at improving the detection of small targets. In this survey, we summarize the state-of-the-art YOLO-based small object detection methods. This review presents a systematic categorization of YOLO-based approaches for small-object detection, organized into four methodological avenues, namely attention-based feature enhancement, detection-head optimization, loss function, and multi-scale feature fusion strategies. We then examine the principal challenges addressed by each category. Finally, we analyze the performance of these methods on public benchmarks and, by comparing current approaches, identify limitations and outline directions for future research.