WA-YOLO: Water-Aware Improvements for Maritime Small-Object Detection Under Glare and Low-Light

Abstract

Maritime vision systems for unmanned surface vehicles confront challenges in small-object detection, specular reflections and low-light conditions. This paper introduces WA-YOLO, a water-aware training framework that incorporates lightweight attention modules (ECA/CBAM) to enhance the model’s discriminative capacity for small objects and critical features, particularly against cluttered water ripples and glare backgrounds; employs advanced bounding box regression losses (e.g., SIoU) to improve localization stability and convergence efficiency under wave disturbances; systematically explores the efficacy trade-off between high-resolution input and tiled inference strategies to tackle small-object detection, significantly boosting small-object recall (APS) while carefully evaluating the impact on real-time performance on embedded devices; and introduces physically inspired data augmentation techniques for low-light and strong-reflection scenarios, compelling the model to learn more robust feature representations under extreme optical variations. WA-YOLO achieves a compelling +2.1% improvement in mAP@0.5 and a +6.3% gain in APS over YOLOv8 across three test sets. When benchmarked against the advanced RT-DETR model, WA-YOLO not only surpasses its detection accuracy (0.7286 mAP@0.5) but crucially maintains real-time performance at 118 FPS on workstations and 17 FPS on embedded devices, achieving a superior balance between precision and efficiency. Our approach offers a simple, reproducible and readily deployable solution, with full code and pre-trained models publicly released.