In response to challenges in practical road target detection, such as low accuracy in detecting small targets and the occurrence of missed and false detections for occluded targets, an improved YOLOv5s road target detection algorithm, termed YOLOv5s-OEAG, is proposed in this study. The label assignment strategy of YOLOv5s is replaced with a more efficient OTA label assignment strategy to enhance the model′s detection accuracy and generalization ability. Additionally, a lightweight decoupled prediction head is introduced to decouple classification and regression tasks for different-sized feature layers, thereby improving the model′s capability to detect small targets on roads. The original nearest-neighbor interpolation upsampling module is replaced with the lightweight and versatile CARAFE module to better preserve fine details in the image, thereby enhancing the model′s accuracy. Furthermore, a novel C3 module, GMC3, is proposed to reduce model computational complexity while improving the model′s feature capturing capability. To enhance the model′s generalization ability, the KITTI dataset is augmented, increasing the number of small targets. Experimental results demonstrate that the improved model achieves a mAP of 90.4% on the augmented KITTI dataset, representing a 2.8% improvement over the original model′s accuracy. With a frame per second (FPS) rate of 75, meeting real-time requirements, the model exhibits enhanced adaptability to complex traffic scenarios.