Abstract
On the basis of analyzing the structure of common power fittings in high-voltage transmission lines and their image features, combined with the DNN deep neural network in machine learning, we proposed a model suitable for high-voltage transmission line inspection robots to identify the types of electric power fittings on the transmission lines. And design a fast ROI generation method suitable for recognizing fittings on power transmission lines. Then we verify the feasibility and rationality of the fitting identification model.
Highlights
High-voltage transmission line inspection robots usually move back and forth on the wires through a mechanical structure
Harr-like features combined with Adaboost cascade classifier were used for preclassification, and Histogram Of Gradient (HOG) features combined with SVM method was used for secondary classification to realize the recognition of transmission line fittings
Aiming at the recognition of line fittings by the highvoltage transmission line patrolling robot, the HOG feature operator is used to study the recognition model combined with DNN
Summary
High-voltage transmission line inspection robots usually move back and forth on the wires through a mechanical structure. Harr-like features combined with Adaboost cascade classifier were used for preclassification, and HOG features combined with SVM method was used for secondary classification to realize the recognition of transmission line fittings. Qi Y [6] proposed an improved SSD model based detection method of line fittings in aerial inspection images of transmission lines, which solved the problem of a large number of missed detection caused by the too small proportion of fittings in the image. Compared with Conventional Neural Networks, Faster R-CNN and YOLO network are faster in recognition These two models usually need a large amount of data to train the model so as to achieve better results. According to the specific needs of a certain patrol robot, we decide to realize the recognition of fittings by the features of HOG with trained DNN networks which can recognize the fittings faster without extremely large amount of image data
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