Research on antenna dip angle recognition method based on improved YOLOv5

In order to achieve efficient and accurate measurement of antenna dip angle and meet the large-scale and efficient measurement requirements in wireless optimization operation and maintenance scenarios, the YOLOv5 target detection framework was cleverly applied in the complex scenario of antenna dip angle measurement, and it was improved to make it suitable for complex antenna detection and attitude recognition tasks, and accurately predict the dip angle. Experimental results show that the improved YOLOv5 model has the same detection capability as the original version, while its downdip prediction error is reduced by 13%, and the absolute prediction error is 0.635°. The improved YOLOv5 model not only guarantees high accuracy, but also significantly improves the measurement accuracy of antenna dip angle, providing a new technical path and reference basis for wireless optimization intelligent operation and maintenance.