复杂天气下融合边缘增强与内容感知特征重构的道路目标检测方法

Edge-Enhanced and Content-Aware Feature Reconstruction for Road Object Detection in Adverse Weather

摘要: 复杂天气条件下，道路图像容易出现对比度下降、边缘模糊和雨雪噪声干扰等退化现象，导致目标检测模型出现漏检、误检和定位不稳定等问题。针对上述问题，本文基于YOLO11提出一种融合边缘增强与内容感知特征重构的道路目标检测方法WRA-YOLO。首先，在骨干网络前端引入深度可分离小波卷积模块(Depthwise Wavelet Transform Convolution, DW-WTConv)，通过离散小波变换显式分解低频结构与高频边缘信息，缓解下采样造成的细节丢失；其次，设计内容自适应特征重校准模块(Block Attention Residual, BlockAttnRes)，利用可学习伪查询向量与RMSNorm对多层特征进行动态加权融合，降低复杂天气背景噪声对特征融合的干扰；最后，引入最小点距离交并比(Minimum Point Distance IoU, MPDIoU)损失函数，以角点距离约束优化边界框回归。实验结果表明，WRA-YOLO在DAWN数据集上的mAP@0.5由72.28%提升至75.42%，在RTTS数据集上的mAP@0.5由63.40%提升至65.56%，说明该方法在雾、雨、雪和沙尘暴等复杂天气道路场景中具有较好的检测适应性。

Abstract: Road images captured under adverse weather often suffer from low contrast, blurred boundaries and rain/snow noise, which degrades the robustness of object detectors. To address this issue, this paper proposes WRA-YOLO, an improved YOLO11-based detector integrating edge enhancement and content-aware feature reconstruction. First, a depthwise wavelet transform convolution module (DW-WTConv) is introduced into the early backbone to explicitly decompose low-frequency structures and high-frequency edge details, thereby reducing detail loss caused by downsampling. Second, a content-adaptive feature recalibration module (BlockAttnRes) is designed to dynamically fuse multi-level features using learnable pseudo-query vectors and RMSNorm, suppressing noisy semantic responses in adverse weather. Finally, MPDIoU loss is employed to improve bounding box regression by constraining corner-point distances. Experiments show that WRA-YOLO improves mAP@0.5 from 72.28% to 75.42% on DAWN and from 63.40% to 65.56% on RTTS, demonstrating better adaptability to road scenes under fog, rain, snow and sandstorm conditions.