Deep learning transmission line defect recognition based on improved Yolov5

Aiming at the problem of slow and inaccurate positioning of defects based on deep learning transmission line defect recognition model, an intelligent detection method based on improved Yolov5 transmission line image recognition is proposed. Firstly, according to the relevant characteristics of the transmission line data set, relevant features are extracted to obtain sufficient sample data. Secondly, the lightweight model of convolutional neural network is constructed, and the model training is completed by using the obtained transmission line sample data. Finally, the location and classification of transmission line defects in complex background are realized through multiple transmission line training sets and test sets. The results show that the proposed algorithm has the highest detection accuracy, and the average detection accuracy can reach 98.7 %. It is a simple, effective and practical transmission line defect recognition method.