To address the problems related to target detection of electric tricycles in road traffic management in China and the shortcomings of current detection models in small target detection and real-time performance, this study proposes a detection method based on an improved YOLOv5s model. The original YOLOv5s model is first improved by adding a small object detection head and by introducing a Transformer structure that combines an efficient additive attention mechanism, and then a dataset based on urban road scenes is built. The model is improved in terms of accuracy, recall, and mean average precision (mAP@0.5) by 0.67%, 2.68%, and 5.78%, respectively. The model also achieves a frame rate of 92 frame/s and demonstrates good processing capabilities, thus meeting the real-time detection requirements for actual road traffic situations.