Objective Detecting fractures along roadway sections of coal mines allows for the characterization of the geologic conditions of roadways, thus providing guidance for roadway tunneling and support engineering. However, conventional image processing methods for fractures along roadway sections are susceptible to complex environments in underground coal mines, exhibiting a low fracture detection efficiency.
Methods This study proposed a novel image processing method for detecting fractures along roadway sections based on a DC-UNet model. This method can be deployed on a mobile terminal and an embedded device, thereby allowing for real-time and effective detection of fractures along roadway sections and enhancing detection efficiency. First, an image dataset of fractures along roadway sections was established. The dataset was then enhanced using image data augmentation techniques, and the images in it were labeled. Subsequently, a framework for the detection and parameter computation of fractures was established based on the DC-UNet architecture. The improved DC-UNet model featured significantly elevated lightweight nature and detection accuracy by improving depthwise separable convolution (DwConv) and incorporating the convolutional block attention module (CBAM)—a dual attention module. Specifically, DwConv was employed to replace conventional convolution operations, reducing the model's parameters and computational load. The CBAM, integrating channel attention with spatial attention, enhanced the model's ability to capture semantic information related to fractures in low-light, dusty, and complex underground environments. Finally, the proposed method was compared with existing image processing algorithms, and the results were validated by importing the DC-UNet model into mobile terminal devices.
Results and Conclusions The results indicate that the improved model achieves a fracture detection accuracy of 92%, higher than that of conventional image processing methods. The improved model, after being deployed on mobile devices, shows a data size of 7.52 MB, which is reduced by 68.9% compared to the original model, suggesting a decreased computational load. This model occupies a space of 19.43 MB, representing a reduction of 80.2% compared to the original model, suggesting an enhanced lightweight nature. This model exhibits a detection duration of 0.075 s, satisfying the requirements for field real-time inspection and accelerating the detection speed. Overall, the proposed method can be applied to detect fractures along the roadway sections in underground coal mines, laying a foundation for the fracture detection and tunneling engineering of roadways in coal mines.
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