Abstract
With the rapid development of urbanization, the role of urban transportation systems has become increasingly prominent. However, traditional methods of traffic management are struggling to cope with the growing demands of traffic and the complexity of urban environments. In response to this situation, we propose the YOLOv8-BCC algorithm to address existing shortcomings. Leveraging advanced technologies such as CFNet, CBAM attention modules, and BIFPN structure, our algorithm aims to enhance the accuracy, real-time performance, and adaptability of urban traffic intelligent detection systems. Experimental results demonstrate significant improvements in detection accuracy and real-time performance compared to traditional methods. The introduction of the YOLOv8-BCC algorithm provides a robust solution for enhancing urban traffic safety and intelligent management.