The “Road Traffic Safety Law of the People’s Republic of China” requires that motorcycle riders and passengers must wear safety helmets as stipulated by law. Consequently, the demand for intelligent visual detection technology for helmet wearing has emerged. This paper focuses on the study of helmet wearing by riders in traffic surveillance video images, based on the YOLO object detection framework. Initially, a branch absorption module is employed to improve the residual backbone network. Subsequently, the convolutional layer feature fusion is enhanced through structural channel recombination. Finally, a designed structural fusion pruning technique is applied to further compress the model’s hyperparameters. Experimental results indicate that the algorithm boasts superior accuracy and real-time performance, with effective detection of small targets. The average precision for multi-classification reaches 88.8%, and the detection speed can achieve up to 29.5 frames per second, which essentially meets the requirements of traffic video surveillance.