Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images.

Abstract

The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.

Plant phenotyping relevance

熱赤外画像と深層学習により、植物のストレス状態および樹冠温度を推定・分類する手法が研究の中心であり、植物表現型の取得・解析手法として適格。

Code and data availability

The paper's infrared thermal images, gas concentration data, and GLNB model code are not publicly deposited; the Data Availability Statement states they are available only on request from the corresponding author.

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