Table 4 - <p>AP.</p>

To address the problem of insufficient detection accuracy for dense targets, small targets and partially occluded objects in complex road scenarios, an improved object detection model, SSH-YOLO, is proposed. On the basis of YOLOv8n, the model optimizes and improves performance through a three-level collaborative architecture: 1) introduce the spatial and deep conversion (SPDConv) module in the backbone network to replace the traditional step downsampling with nonstep convolution, retain the fine-grained features of small targets, and solve the feature loss problem of low-resolution images; 2) embed the spatial and channel collaborative attention module (SCSA), through cross-scale feature fusion (SMSA) and channel weight progressive optimization (PCSA), focus on the key visible areas of the occluded target and suppress background interference such as roadside vegetation; and 3) add a new 160 × 160 resolution small object detection head, combined with the original P3‒P5 layer to form a four-level detection system, covering long-distance small targets < 32 × 32 pixels. The experimental results show that the improved model performs well on the self-built RoadScene-Complex dataset and four public datasets BDD100K: 0.729 (12.4% higher than YOLOv8n) on the RoadScene-Complex dataset mAP@0.5 (12.4% higher than YOLOv8n) and 0.868 (7.6% higher than the KITTI dataset) mAP@0.5). COCO small target subset mAP@0.5 to 0.585 (up 16.5%), and CityPersons occluded scene mAP@0.5 to 0.739 (up 22.8%). At the same time, it maintains lightweight characteristics and has an inference speed of up to 60 FPS to meet the needs of real-time on-board detection. The research results provide a balanced solution of “accuracy-speed-lightweight” for high-precision target detection in complex traffic scenarios, especially in small target and occlusion scenarios.针对复杂道路场景中密集目标、小目标与部分遮挡物体的检测精度不足问题，本文提出一种改进的目标检测模型SSH-YOLO。该模型以YOLOv8n为基础，通过三级协同架构优化并提升模型性能：1）在主干网络中引入空间与深度转换模块（Spatial and Deep Conversion, SPDConv），以非步长卷积替代传统的步降下采样，保留小目标的细粒度特征，解决低分辨率图像的特征丢失问题；2）嵌入空间与通道协同注意力模块（Spatial and Channel Collaborative Attention, SCSA），通过跨尺度特征融合（SMSA）与通道权重渐进优化（PCSA），聚焦遮挡目标的关键可见区域，抑制路边植被等背景干扰；3）新增160×160分辨率的小目标检测头，结合原有的P3-P5层构成四级检测系统，可覆盖尺寸小于32×32像素的远距离小目标。实验结果表明，改进后的模型在自建的RoadScene-Complex数据集以及BDD100K、KITTI、COCO、CityPersons四个公开数据集上均取得优异表现：在RoadScene-Complex数据集上的mAP@0.5为0.729（较YOLOv8n提升12.4%）；在KITTI数据集上的mAP@0.5为0.868（提升7.6%）；在COCO小目标子集上的mAP@0.5达到0.585（提升16.5%）；在CityPersons遮挡场景数据集上的mAP@0.5达到0.739（提升22.8%）。同时该模型保持轻量化特性，推理速度可达60 FPS，能够满足车载实时检测的需求。本研究成果为复杂交通场景（尤其是小目标与遮挡场景）下的高精度目标检测提供了一种兼顾精度、速度与轻量化的平衡解决方案。