针对雾天高速公路场景智能车辆环境感知识别精度偏低、多尺度目标与远距离小目标易漏检的问题，提出了一种基于细节增强与特征保留的雾天高速公路车辆检测算法。首先，以DEA-Net算法为框架对图像进行去雾预处理，在内容引导注意力模块CGA中引入门控融合自适应模块（GAF），通过门控生成器动态加权通道与空间注意力，实现精准去雾与车辆纹理细节的增强，有效提升去雾图像质量；其次，在目标检测部分，对YOLOv13n的主干和颈部网络引入拆分双支保留型下采样模块（SDPDown），实现车辆局部轮廓细节与全局上下文特征的协同保留；最后，引入细节增强高速公路车辆检测头（DEHV），捕捉车辆多尺度边缘与纹理细节，提升复杂交通场景下车辆目标的识别精度，并将改进后的模型命名为SD-YOLO。结果表明，改进DEA-Net相比于原算法，峰值信噪比（PSNR）和结构相似性（SSIM）分别提高了0.71dB和0.46个百分点；SD-YOLO相较于基线方法YOLOv13n，精确率、召回率和mAP@0.5分别提升了2.7、7.8和5.0个百分点，参数量和浮点计算量分别降低至1.9M和5.5G，且每秒检测图像127帧。实验结果表明所提出算法在雾天车辆检测的精度上优于目前主流算法，为雾天高速公路智能车辆环境感知提供可靠的车辆检测技术方案。

To address the issues of low recognition accuracy in intelligent vehicle environmental perception under foggy highway conditions, along with frequent missed detections of multi-scale and distant small targets, a foggy highway vehicle detection algorithm based on detail enhancement and feature preservation is proposed. Firstly, the DEA-Net framework is adopted for image defogging preprocessing. A Gated Adaptive Fusion (GAF) module is incorporated into the Content-Guided Attention (CGA) mechanism, where a gated generator dynamically weights channel and spatial attention. This enhancement achieves precise defogging and strengthens vehicle texture details, effectively improving the quality of defogged images. Secondly, in the object detection stage, a Split Dual-Path Downsampling (SDPDown) module is introduced into the backbone and neck networks of YOLOv13n, enabling the collaborative preservation of both local contour details and global contextual features of vehicles. Finally, the Detail Enhancement Highway Vehicle Detection Head (DEHV) is introduced to capture the multi-scale edge and texture details of vehicles, improve the recognition accuracy of vehicle targets in complex traffic scenarios, and the improved model is named SD-YOLO. The results show that compared with the original algorithm, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the improved DEA-Net algorithm are increased by 0.71dB and 0.46 percentage points respectively. Compared with the baseline method YOLOv13n, SD-YOLO has improved the precision rate, recall rate and mAP@0.5 by 2.7, 7.8 and 5.0 percentage points respectively, reduced the parameter quantity and floating-point calculation amount to 1.9M and 5.5G respectively, and detected 127 frames of images per second. The experimental results show that the proposed algorithm is superior to the current mainstream algorithms in terms of vehicle detection accuracy in foggy weather, providing a reliable vehicle detection technical solution for intelligent vehicle environmental perception on foggy expressways.