Electric Tricycle Detection Based on Improved YOLOv5s Model

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.