Robustness analysis of YOLO and faster R-CNN for object detection in realistic weather scenarios with noise augmentation.

Object detection systems are central to the autonomy and safety of intelligent transportation systems. Yet, the accuracy of object detection models can suffer under environmental noise or adverse weather. This paper tested the robustness of four object detection architectures: YOLOv5s, YOLOv8m, YOLOv10n, and Faster R-CNN, to visual degradation (real-world weather and artificial noise). We utilize the DAWN dataset, a benchmark of 1,000 high-resolution traffic images captured under fog, rain, snow, and sandstorms, with further augmentations of Gaussian noise, salt-and-pepper noise, blurriness, and overlays applied with artificial fog. We standardized all annotations to YOLO and COCO annotation formats for multi-framework interoperability. Our quantitative analysis used mAP@0.5, mAP@0.5:0.95, Precision, and Recall to compare the models, alongside some qualitative analysis through visual overlays and plotting training loss. The findings of our analysis showed YOLOv8m achieved the highest baseline accuracy on clean data, while Faster R-CNN proved resilient in noisy environments. YOLOv10n achieves a good trade-off between efficient and robust detection. The results of this study highlight the necessity of adaptive training pipelines and environment-aware benchmarks to enhance the real-world reliability of vision-based detection systems.