Method for identifying insulator defects by pruning YOLOv3 with small on-site sample size

Abstract

The invention provides a method for identifying insulator defects by pruning YOLOv3 with a small on-site sample size. The method comprises the steps: 1, transmitting picture data in real time according to the inspection of an unmanned plane, and obtaining an insulator image and corresponding XML label data; 2, processing the insulator image; 3, performing pruning modification operation by taking YOLOv3 as a basic framework, and then establishing a deep learning model; 4, introducing an SPPnet module composed of four parallel maxpool layers into the pruned YOLOv3, and enabling the SPPnet moduleand the pruned YOLOv3 to be subjected to pruning; 5, converting the processed insulator image and the modified TXT label data into training data; 6, obtaining an insulator recognition model; 7, obtaining insulator picture frame information detected in each picture; and 8, judging the fault of the insulator. The method has the advantages that under the condition that the average precision is not reduced, the training time is shortened, and the requirement for the picture quality is reduced, meanwhile, the requirement for the computing power of a vehicle-mounted server is reduced, and the intelligent level of insulator inspection is improved.

Description

technical field [0001] The invention relates to power grid inspection and maintenance technology, in particular to a YOLOv3 pruning method for identifying insulator defects with a small number of on-site samples. Background technique [0002] With the rapid development of the economy and the increasing progress of smart grid construction, higher requirements are put forward for the intelligent and rapid inspection of transmission lines. As we all know, an insulator is a special insulation control that can play an important role in overhead transmission lines. Its main function is to achieve electrical insulation and mechanical fixation, for which various electrical and mechanical performance requirements are stipulated. However, the process of manual maintenance and overhaul is also extremely cumbersome and has a high risk factor. According to survey statistics, there are many safety accidents caused by insulator defects. Therefore, regular inspection of tower insulators is...

AI Technical Summary

Problems solved by technology

Although this method solves the danger of manual inspection, the screening work in the later stage is still cumbersome and complicated, and manual inspection of pictures due to problems such as picture quality and clarity will cause many missed and misjudged results

Later, the artificial intelligence algorithm based on the deep learning model was gradually used for image recognition and screening. However, different inspection sites have different characteristics, and the same set of learning results cannot always be used. The small number of on-site samples and fewer effective samples have become unavoidable. question

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