transformer target detection and appearance defect identification method based on VGG-net style migration

Abstract

The invention discloses transformer target detection and appearance defect recognition method based on VGG-net style migration, and relates to the field of image recognition. At present, the appearance of the equipment in the substation is mainly based on the image acquisition of the robot. Due to the image acquisition error, the appearance detection may be offset, resulting in inaccurate appearance detection. In addition, the collected equipment images are mostly positive samples, including rust, oil leakage and the like. There are fewer negative samples of appearance defects, which will leadto model training over-fitting, poor generalization ability, and easy to cause false detection. The method first collects samples and constructs a sample set; then uses the SSD target detection algorithm to accurately intercept the target device for detecting appearance defects; and then uses the VGG-net-based style migration algorithm to generate defect samples for the problem of insufficient negative samples. The sample set is extended to improve the generalization ability of the discriminant model; finally, the appearance is detected according to Le-net's discriminant network. Transformertarget detection and appearance defect identification are accurately realized.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a transformer target detection and appearance defect recognition method based on VGG-net style transfer. Background technique [0002] At present, the condition-based maintenance technology of substation equipment mainly judges the operation status of equipment based on electrical quantities such as voltage and current, and physical quantities such as oil temperature and oil pressure. Graph technology to judge the health status of equipment. For highly intelligent substations, intelligent inspection robots have been gradually deployed, which can realize automatic inspections and collect defect images. For most substations, engineers usually use tools such as cameras, handheld terminals, and cameras to collect images of transformer defects. Engineers tag images and describe defect events in a log, often including events such as oil spills, corrosion, switches opening and closing...

AI Technical Summary

Problems solved by technology

However, due to the travel error of the image acquisition equipment, the target equipment does not necessarily appear in the fixed position of the captured image, and the appearance detection may be offset, resulting in inaccurate appearance detection

[0004] 2. Insufficient number of negative samples

However, most of the collected equipment images are positive samples, and there are few negative samples containing appearance defects such as rust and oil leakage.

A small number of negative samples will lead to overfitting of model training, poor generalization ability, and easy to cause false detection in application

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