Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph Network Status Based on Improved YOLO Algorithm

With the rapid development of high-speed railway technology, ensuring its operational safety has become an important issue. In particular, real-time monitoring of the high-speed railway pantograph network system is of great significance for preventing failures and reducing accidents. This research aims to improve the intelligent detection performance of high-speed railway pantograph network status through the improved YOLO algorithm. Research methods include the use of deep learning technology and image processing technology, focusing on improving the YOLO algorithm to enhance its detection accuracy in complex environments, especially its ability to identify small targets and its adaptability to dynamic environments. It is expected that through these improvements, more accurate and efficient status monitoring of high-speed railway pantographs will be achieved, thereby improving the safe operation level of high-speed railways.