Catenary insulator defect detection based on contour features and gray similarity matching

Insulators are the key components of high speed railway catenaries. Insulator failures can cause outages and affect the safe operation of high speed railways. It is important to perform insulator defect detection. Due to the collection of insulator images by moving catenary inspection vehicles, the consistency of the images is poor, and the number of insulator defect samples is very small. An algorithm of deep learning and conventional template matching cannot meet the requirements of insulator defect detection. This paper proposes a fusion algorithm based on the shed contour features and gray similarity matching. High accuracy and consistency of contour extraction and precise separation of each insulator shed were realized. An insulator defect detection model based on the spacing distance of the sheds and the gray similarity was constructed. Experiments show that the method based on the contour features and gray similarity matching can effectively classify normal insulators and defective insulators. Recall of 99.50% and high precision of 91.71% were achieved in the test of the image data set, and this can meet the requirements for the reliability and high precision of a detection algorithm for catenary insulators.