Research and application of electric power inspection technology based on intelligent wearable

At present, the information management of substation protection hard plate is totally dependent on manual inspection. With the rise of AI to national strategy and the further development of in-depth learning technology, a plate state recognition method based on in-depth learning OpenCV+SSD network model is proposed. Firstly, on the basis of image binarization, corner detection of protective screen cabinet is carried out based on Hough linear detection algorithm. Through perspective transformation, the plate image is corrected, so as to avoid the influence of the angle change of the photographed image on the recognition results. Secondly, the template matching method is used to segment the image of the press plate, and the mapping relationship between different press plates and their functions is established to improve the accuracy of the state detection of the press plate. Then, a target detection model of SSD is built based on the TensorFlow deep learning framework, and the accuracy and generalization ability of the training model are improved by adjusting the parameters continuously and using regularization processing. The test results show that the target detection accuracy and recall rate of this method are both greater than 0.95. Compared with the traditional opencv and Hog + SVM processing methods, the recognition effect of press plate state has been significantly improved.