Unmanned aerial vehicle patrol detection image power small component identification method and system based on Faster R-CNN

Abstract

The invention discloses an unmanned aerial vehicle patrol detection image power small component identification method and system based on Faster R-CNN. The method comprises the following steps: carrying out pre-training on a ZFnet model, and extracting a feature graph of an unmanned aerial vehicle patrol detection image; training an RPN region proposed network model obtained through initialization to obtain a region extraction network, generating a candidate region frame on the feature graph of the image by utilizing the region extraction network, and carrying out feature extraction on the candidate region frame to extract position features and in-depth features of a target; carrying out training on a Faster R-CNN detection network obtained after initialization by utilizing the position features and in-depth features of the target and the feature graph to obtain a power small component detection model; and carrying out actual power small component identification detection by utilizing the power small component detection model. The beneficial effects are that Faster R-CNN is utilized to realize identification and positioning of a plurality of types of power small components, so that identification speed of about 80 ms per picture and 92.7% accuracy can be achieved.

Description

technical field [0001] The invention relates to a Faster R-CNN-based method and system for identifying power widgets in unmanned aerial vehicle inspection images. Background technique [0002] In recent years, with the gradual popularization of UAV (Unmanned Aerial Vehicle, UAV) applications, power line inspection drones have received extensive attention from major power grid companies and have been demonstrated and promoted. UAV line inspection has the characteristics of low risk, low cost and flexible operation in field operations; at the same time, it also brings an increase in the workload of line inspection operations in the industry, making massive data require a lot of manual interpretation to obtain the final inspection Report. [0003] At present, power component recognition still stays at the traditional shallow feature-based recognition level, through finely designed shallow features, such as SIFT (Scale-invariant feature transform), edge detector, HOG (Histogram...

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Problems solved by technology

[0005] Chinese invention patent (application number: 201510907472.X, patent name: a method for identifying small parts of transmission lines), although this method can realize the identification and positioning of spacers

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