Hidden Danger Detection and Identification System of Power Transmission Tower Based on YOLOV11

This study proposed a hidden danger detection and identification system of power transmission tower based on YOLOv11, aiming to introduce an intelligent detection and identification system of hidden danger of power transmission tower based on YOLOv11 algorithm, which is committed to enhancing the safety and stability of power transmission tower. YOLOv11, the latest and most advanced object detection algorithm in the YOLO family, is known for its excellent detection efficiency and accuracy. In this system, YOLOv11 is specially trained to accurately identify four potential hazards on power pylons: bird's nests, balloons, garbage and kites. The accuracy of the system is as high as 93.8%, and the recall rate has reached an excellent level of 73.3%. In order to fully verify the actual utility of the model, we carefully built a user-friendly intuitive interface (UI) using PyQt5. The system integrates image detection, video detection and real-time camera monitoring and recognition functions, can accurately identify the above four types of hidden dangers, and comprehensively record all the detection results, which provides great convenience for subsequent data analysis and processing. This system is not only easy to operate, user-friendly user interface design, but also shows excellent performance in the process of real-time monitoring and recognition, ensuring a high degree of accuracy. These characteristics have laid a solid foundation for the subsequent related work.