In the heart of China’s mining industry, a breakthrough in fire detection technology is set to revolutionize safety and operational efficiency. Researchers at the School of Artificial Intelligence, China University of Mining and Technology-Beijing, have developed a novel method to distinguish between mine fires and interference light sources, potentially saving lives and preventing costly downtime. The lead author, Jiping Sun, and his team have published their findings in the journal Meitan kexue jishu, which translates to Coal Science and Technology.
Mining operations are fraught with hazards, and fire is one of the most significant threats. Traditional image monitoring systems, while effective, often struggle with false positives due to interference from mine lighting. This is where Sun’s research comes in. The team has proposed a method based on the “depression degree” of images, a metric that calculates the ratio of the total concavity area of the target image boundary to the actual area of the target image.
“The depression degree method is not affected by the distance and image size between the camera and the detection target, the installation position and angle of the camera, or the shape of the mine light source,” Sun explained. This adaptability is a game-changer, as it allows for more accurate and reliable fire detection in the complex and dynamic environment of a mine.
So, how does it work? The method identifies multiple depression areas on the boundary of the flame image, which are absent in mine light source images. By calculating the depression degree, the system can distinguish between flames and interference light sources with high accuracy. In tests, the depression degree method achieved an impressive 98.2% accuracy and a 98.4% recall rate, outperforming other methods like circularity and rectangularity recognition.
The implications for the energy sector are substantial. Mines are the lifeblood of the energy industry, supplying coal and other resources that power our world. However, fires can bring operations to a halt, causing significant financial losses and putting workers at risk. With more accurate fire detection, mines can operate more safely and efficiently, reducing downtime and preventing disasters.
But the potential applications don’t stop at fire detection. The depression degree method could be adapted for other image recognition tasks in mining, such as detecting structural damage or monitoring equipment wear and tear. As Sun puts it, “This method has strong adaptability and high recognition accuracy, making it a valuable tool for various applications in the mining industry.”
The research published in Meitan kexue jishu, marks a significant step forward in mining technology. As the industry continues to evolve, innovations like this will be crucial in ensuring safety, efficiency, and sustainability. The future of mining is bright, and with advancements like Sun’s depression degree method, it’s looking even brighter.
