Suspicious sample identification method based on power transmission line channel image continuous alarm

Abstract

The invention discloses a suspicious sample identification method based on power transmission line channel image continuous alarm. The method includes: using an image analysis model to identify an image acquired by certain visual inspection equipment in real time; and judging the identification result by using a continuous alarm model, and determining whether the continuous alarm is triggered or not: marking the image identified as having no hidden danger as a suspicious missed alarm sample if the continuous alarm is triggered, and marking the image identified as having hidden danger as a suspicious missed alarm sample if the continuous alarm is not triggered. According to the invention, continuous alarm identification and judgment is carried out on relatively fixed hidden dangers of staying areas such as machinery, smoke and foreign matters with limited operation ranges such as tower cranes and excavators, whether to trigger continuous alarm or not is judged in combination with imagedata continuously acquired by visual equipment, abnormal samples are marked, and suspicious missed alarm or suspicious false alarm samples are determined. According to the invention, the intelligent level of power transmission line operation inspection is improved by improving the recognition accuracy of the image analysis model.

Description

technical field [0001] The invention relates to a suspicious sample identification method based on continuous alarming of transmission line channel images, and belongs to the technical field of transmission line intelligent inspection. Background technique [0002] With the upgrading of transmission line maintenance technology, the visual inspection of transmission line channels has been widely used. At present, the automatic identification of visual images and the marking of alarm objects appearing in the images have been realized, such as tower cranes and excavators and other machinery, smoke, and foreign objects. class etc. However, there are certain false positives and false negatives in the image analysis model, which requires continuous optimization and upgrading. For this reason, a large number of false negatives and false positive samples need to be collected. The collection process requires manual screening and confirmation, which consumes a lot of manpower, and the...

AI Technical Summary

Problems solved by technology

However, there are certain false positives and false negatives in the image analysis model, which requires continuous optimization and upgrading. For this reason, a large number of false negatives and false positive samples need to be collected. The collection process requires manual screening and confirmation, which consumes a lot of manpower, and the manual screening process Influenced by subjective factors

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