Despite significant advances in object detection technology, vulnerable pedestrian detection in intelligent transportation systems remains highly challenging under complex weather conditions. Environmental factors such as fog, rain, and snow often lead to occlusion, motion blur, and low-contrast images, making small-scale or weak-featured vulnerable pedestrians difficult to accurately identify. Therefore, improving the detection accuracy and robustness of vulnerable pedestrians in complex weather scenarios has become an urgent research problem. To address this issue, this paper proposes an improved YOLOv8-based vulnerable pedestrian complex weather detection algorithm, termed VPCW-YOLO. The proposed method enhances detection performance through multiple structural optimizations. First, a C2f_ST module is designed by integrating Spatial and Channel Reconstruction Convolution (SCConv) with a triplet attention mechanism to strengthen feature representation and improve the model’s focus on critical regions. Second, a residual self-attention based (RSAB) module based on a self-attention mechanism is introduced to enhance global feature modeling capability under complex weather conditions. In addition, a Space-to-Depth operation is embedded into the backbone network to preserve more fine-grained information. Finally, a P2 small-object detection layer is added to improve the detection performance for distant and tiny pedestrians. Experimental results on the augmented BGVP dataset demonstrate that VPCW-YOLO achieves 73.0% mean Average Precision (mAP)@0.5 and 49.2% mAP@0.5:0.95, representing improvements of 5.4% and 5.7%, respectively, compared with the original YOLOv8 model. Generalization experiments on the Real-world Traffic Sign Detection in the Wild dataset (RTTS) pedestrian subset show that VPCW-YOLO achieves a 6.3% improvement in mAP@0.5. The results indicate that the proposed method effectively improves the detection accuracy and robustness of vulnerable pedestrians in complex weather scenarios while maintaining certain generalization capability, providing a promising solution for pedestrian safety perception in intelligent transportation systems.

The Intelligent Transportation System (ITS), as a vital means to alleviate traffic congestion and reduce traffic accidents, demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things (IoT) technologies. The enhancement of its performance largely depends on breakthrough advancements in object detection technology. However, current object detection technology still faces numerous challenges, such as accuracy, robustness, and data privacy issues. These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions. This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS, aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems. To achieve this objective, we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to search, screen, and assess the eligibility of relevant literature, ultimately including 88 studies. Through an analysis of these studies, we summarized the characteristics, advantages, and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage. Additionally, we examined its applications in ITS from three perspectives: vehicle detection, pedestrian detection, and traffic sign detection. We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues. This review offers researchers a comprehensive perspective, identifying potential improvement directions for object detection technology in ITS, including accuracy, robustness, real-time performance, data annotation cost, and data privacy. In doing so, it provides significant guidance for the further development of IoT-based intelligent transportation systems.