Road traffic accidents have become a global problem. Crosswalks and intersections are small road targets in remote sensing images and, as an important part of the roadway, also have a high incidence of traffic accidents. The recognition of the crosswalks and intersections in high-resolution remote sensing images with more detailed information can provide drivers with timely information to prevent or reduce traffic accidents. Aiming at the difficulty of detecting small road targets in remote sensing images, an improved model based on YOLOv5—RS-YOLOv5 is proposed. Specifically, we designed a cross-layer feature enhancement module in the neck to capture the position information of small targets, allowing the model to locate small targets more accurately. Meanwhile, a bi-directional feature pyramid network is used as a feature fusion network to more fully obtain contextual information. Finally, to pay more attention to small targets during training and alleviate the imbalance between positive and negative samples of small targets, a loss function combining normalized Wasserstein distance loss and intersection over union loss is designed to improve the positioning loss. In the experiment, we evaluate models on two remote sensing datasets (Potsdam and Shanghai remote sensing datasets), with mAP50 reaching 69.55% and 62.9%, respectively. Experimental results demonstrate the effectiveness of the model.