Algorithms of component recognition and condition assessment in power inspection image

The stable operation of power system is very important to the society, but the traditional manual inspection method has the problems of low efficiency, high risk, limited accuracy and high cost. With the rapid development of deep learning and image recognition technology, intelligent inspection technology has become an important means to improve the security and reliability of power system. This paper analyzes the application of deep learning in image recognition, including convolutional neural network (CNN) and fully convolutional neural network (FCN), and discusses the shortcomings of traditional power inspection methods. This paper introduces the process of image recognition in detail, including message processing, Json processing, tag recognition, pixel classification, row and column cutting and other steps, and discusses its application in power components such as towers, fittings and wires. In addition, the accuracy and efficiency of image recognition technology in practical applications are evaluated, and the challenges faced by current technology, such as environmental interference and equipment limitations, are pointed out.