An algorithm for detecting smoke and dust in open-pit coal mines based on YOLOv5

Coal mining in open-pit mines can lead to spontaneous combustion due to the accumulation of coal. During the early stages of spontaneous combustion, smoke is generated, and its diffusion characteristics are similar to the dust stirred up by vehicles in the coal yards. The diffusion of smoke not only affects the safety of coal production but also poses health risks to the staff. Currently, most smoke detection algorithms focus on fire backgrounds, including both flames and smoke targets. However, there is limited research on smoke detection in the environment of open coal yards. The unique characteristics of the coal yard environment, such as the easy diffusion of smoke and dust with no distinct boundaries, make smoke detection challenging. The conventional smoke detection algorithms are not suitable for detecting smoke targets in coalfield environments. To address this issue, this paper proposes the M-ST-YOLOv5 model (Mobile-SwinTransformer-YOLOv5). Firstly, incorporates the concept of the MobileNetV2 network architecture to reduce the complexity of practical deployment. Secondly, the Swin-Transformer module is added to enhance the network's recognition capabilities for smoke and dust. Experimental results demonstrate that the proposed method effectively reduces the number of network parameters while improving the smoke detection rate.