Research on Mine Smoke Detection Technology Based on Multi-Feature Fusion Analysis

Abstract

Traditional smoke detection sensors are characterized by low sensitivity, poor stability, etc. In this study, we propose a coal mine smoke detection technique based on multi-feature fusion analysis. Detection of smoke on belt conveyors is realized by machine vision technology. Firstly, the inter-frame difference method is used to capture the motion region of the smoke. And the suspected smoke region is obtained. Then, the color features of smoke are obtained by RGB color histogram. The motion direction features of smoke are obtained by smoke optical flow vector extraction. The irregular contour features of smoke are obtained by smoke contour irregularity criterion statistics. Based on obtaining the suspected smoke area, the above three features are used to determine whether the belt conveyor produces smoke. This study collected four video images of the belt surface smoke, stand smoke, light samples, and dust samples. The final combined diagnostic rate was 94.19% by testing the above detection models. This study proposes a stable and effective smoke detection technique for coal mine safety production.