A method for detecting bird's nest of transmission line towers

Abstract

The invention discloses a bird's nest detection method for power transmission line towers, which includes collecting original target images including power transmission line towers; preprocessing the collected original target images; extracting the contours of the preprocessed target images; and judging whether the contours meet conditions. Determine whether there is a bird's nest. If the contour area is greater than the set threshold and the contour is a non-convex hull, then it is determined that the contour is a bird's nest. Based on the image contour, the invention has a simple and efficient algorithm, can effectively detect the bird's nest on the power transmission line, and saves manpower and material resources.

Description

technical field [0001] The invention belongs to the field of image processing and transmission line monitoring, and in particular relates to a detection method for a transmission line tower bird's nest. Background technique [0002] Transmission line towers play a very important role in the power system and are directly related to the electricity consumption of thousands of households. Large-scale power outages will bring immeasurable losses to the national economy. The nesting behavior of birds will affect the safe operation of transmission line towers, which can easily cause short circuit or tripping of lines. [0003] The traditional detection method relies on manual detection. This detection method requires staff to go deep into the field. It is limited by geographical and time constraints, consumes a lot of manpower and material resources, and has limited monitoring coverage. The shape of a bird's nest is often related to its location, ranging from irregular hemispheri...

AI Technical Summary

Problems solved by technology

The nesting behavior of birds will affect the safe operation of transmission line towers, which can easily cause short circuit or tripping of the line

[0003] The traditional detection method relies on manual detection. This detection method requires staff to go deep into the field. It is limited by geographical and time constraints, consumes a lot of manpower and material resources, and has limited monitoring coverage.

The shape of the bird's nest is often related to its location. There are irregular hemispheres and cubes, etc. Existing machine learning algorithms, template matching, feature matching and other methods cannot effectively detect bird's nests.

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