Fog-YOLO11n: A Lightweight Traffic Object Detection Framework for Autonomous Driving Under Foggy Conditions

Abstract

Foggy weather degrades road images by reducing visibility, attenuating object contrast, and blurring boundaries, while autonomous ground vehicles require accurate and lightweight environment perception on resource-constrained onboard platforms. This study proposes Fog-YOLO11n, a lightweight traffic object detector based on YOLO11n. A GhostRTA backbone combines RepGhostNet with the proposed C2RTA module: RepGhostNet reduces redundant computation, whereas C2RTA uses channel statistics and multi-scale spatial context to reinforce weak low-contrast responses. FRISA, a dual-branch interactive attention module, separates semantic responses from residual details and uses bidirectional gates to suppress fog- and reflection-related textures while preserving object contours. SD-MPDIoU adaptively modulates corner-distance penalties according to relative box geometry and reweights samples by localization quality, improving regression stability for ambiguous boundaries. Experiments on the RTTS dataset show improvements of 3.80 and 2.52 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, over YOLO11n, while reducing parameters from 2.59 M to 2.14 M and computation from 6.44 to 5.66 GFLOPs. The resulting accuracy-complexity balance supports real-time foggy-road perception for autonomous unmanned vehicles.