Aiming at the problems of missed targets, inaccurate target localization, insufficient target feature expression, and unsatisfactory target recognition effects in the current object detection algorithms for autonomous driving in traffic scenarios, a road object detection method based on TPH-YOLOv5 is proposed. Firstly, to reduce the risk of missed objects caused by drastic changes in object scales, a detection head for detecting small objects is added, and a Transformer prediction head is used to capture global information for precise object localization in high-density scenes. Secondly, to enhance the feature expression ability of the model, the output of the convolutional layer is weighted using the SIMAM module. Finally, in order to improve the accuracy of target recognition, four SPP blocks are added to the neck of the network for multi-scale fusion, and the EIOU is used as the bounding box loss function to accelerate convergence speed and improve regression accuracy. Through ablation, comparison, and visualization experiments, it is shown that the proposed algorithm improves the average precision by 8.1% compared to YOLOv5, significantly reduces the missed detection rate, and enhances the object detection performance.