This study develops a model called RA-CRPN for detecting small targets in road vehicle vision. The method addresses the challenges of low detection precision and reliability for small objects detection in road scenes. These objects occupy a small number of pixels and their feature representation information is often insufficient. First, based on the Faster R-CNN framework, the RO-ResNet is integrated into the ResNet50 backbone network, which enabled the output feature blocks to capture contextual information. Second, the RA-ResNet module is added after the backbone network to provide new feature information for each ResNet stage by fusing context information with object features. Then, the improved coarse-to-fine RPN (CRPN) module is utilized to enhance feature alignment and proposal box correction during the two-stage transition, providing high-quality feature information for the region proposal network (RPN) stage. Finally, the SODA-D public small object dataset is employed to validate and analyze the model by comparing it with other methods. The overall average precision (AP), average precision of extremely small (AP_es) and average precision of relative small (AP_rs) of the proposed method are 3.9, 2.4, and 3.4 percentage points , respectively, better than the Faster R-CNN baseline model, indicating improved overall detection precision. Additionally, road object detection tests are conducted using a custom vehicle driving dataset. The results show that the mAP@50 (mean average precision at 50% intersection over union) of the model is 5.8 percentage points higher than that of the Faster R-CNN baseline model, further verifying the precision and robustness of the proposed model.