Rough classification of power equipment images based on BP algorithm

With the development of smart grid technology and camera technology, image data of power equipment has emerged in large numbers. There are a lot of redundancy in these image data and the information value density contained is low, and the efficiency of recognition and classification using traditional manual methods is low. To solve the problems of high cost, low efficiency and subjectivity caused by traditional manual methods. A power equipment identification method based on neural network model of BP algorithm is proposed. BP neural network has strong robustness and learning adaptability, and can efficiently solve massive image recognition and classification problems. First introduce the model structure of the neural network, then explain the process of training the model, and finally analyze the problems that occurred during the entire experiment and the areas worthy of improvement. Experimental results show that the designed model has an accuracy of 85% for insulator identification, which is feasible and practical.