基于OpenCV+SSD深度学习模型的变电站压板状态智能识别

目前,变电站保护硬压板信息管理处于完全依赖于人工巡检的状态.伴随着人工智能上升为国家战略,深度学习技术进一步发展,提出一种基于OpenCV+SSD深度学习模型的压板状态识别方法.在图像二值化并高斯滤波基础上,基于霍夫直线检测算法进行保护屏柜角点检测,并通过透视变换实现压板图像矫正,从而避免拍摄图像的角度变化对识别结果的...

Intelligent identification of substation platen state based onOpenCV + SSD deep learning model

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.