TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection

Abdulkarim Atrash, Seyda Ertekin, Omur Ugur, Omar Moured, Yufan Chen, Jiaming Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 2201-2210

While critical for defense and surveillance, infrared small target detection (IRSTD) remains a challenging task due to: (1) target loss from minimal features, (2) false alarms in cluttered environments, (3) missed detections from low saliency, and (4) high computational costs. To address these, we propose TY-RIST, an optimized YOLOv12n architecture featuring: (1) a stride-aware backbone with finegrained receptive fields, (2) a high-resolution detection head, (3) cascaded coordinate attention blocks, and (4) a branch pruning strategy that reduces computational cost up to 25.5% while marginally enhancing performance and enabling real-time inference. Additionally, we incorporate the Normalized Gaussian Wasserstein Distance (NWD) to improve regression stability. Extensive experiments on four benchmarks and across 20 different models demonstrate state-of-the-art performance, boosting mAP@50 by +7.9%, Precision by +3%, and Recall by +10.2%, while running up to 123 FPS on a single GPU. Cross-dataset validation on a fifth dataset further confirms strong generalization capability. Further results and details are published at www.github.com/moured/TY-RIST.