A power transmission line mountain fire smoke detection method and system

By employing a pre-trained semantic segmentation model and shape, position, and color matching methods on power transmission lines, combined with a microprocessor, accurate identification of wildfire smoke was achieved. This solved the problems of poor detection adaptability and high false alarm rate in existing technologies, and reduced system costs.

CN121053545BActive Publication Date: 2026-02-17STATE GRID HUNAN ELECTRIC POWER CO LTD ELECTRIC POWER SCI RES INST +2
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Patent Information

Application Number
CN202511582455.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-10-31
Publication Date
2026-02-17
Estimated Expiration
2045-10-31

AI Technical Summary

Technical Problem

Existing technologies for detecting wildfire smoke on power transmission lines suffer from poor adaptability, high false alarm rate, high cost, limited coverage, and low accuracy, making it difficult to achieve accurate and reliable wildfire smoke detection.

Method used

A pre-trained semantic segmentation model is used to segment candidate regions of wildfire smoke. Combined with shape matching, location and color distribution comparison, the final smoke confidence score is calculated by weighted summation. The detection method is executed by a microprocessor to reduce false recognition.

Benefits of technology

It achieves accurate identification of smoke from wildfires on power transmission lines, reduces the false identification rate, is suitable for a wide range of applications, and reduces system costs.

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Abstract

The application discloses a power transmission line mountain fire smoke detection method and system, comprising the following steps: for the power transmission channel picture to be detected, a pre-trained semantic segmentation model is used to segment out the mountain fire smoke candidate area in the picture and calculate the semantic segmentation confidence score; for each mountain fire smoke candidate area, the shape similarity score is calculated through a shape matching method, the position confidence score is calculated through a position function, and the color confidence score is calculated through color distribution comparison; the semantic segmentation confidence score, the shape similarity score, the position confidence score and the color confidence score are weighted and summed to obtain the final smoke confidence score, which is compared with a preset threshold to determine whether the corresponding mountain fire smoke candidate area is mountain fire smoke. The technical scheme provided by the application can effectively identify the mountain fire smoke condition in the power transmission line channel picture, and significantly reduce the misidentification conditions such as pond smoke, chimney smoke and cloud and mist, and has a wide application scenario.
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