A DC-UNet-based image processing method for detecting fractures along roadway sections of coal mines

Authors

DONG Zheng, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaFollow
ZHANG Xuhui, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, ChinaFollow
YANG Wenjuan, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, China
KANG Le, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
TANG Duwei, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
TIAN Chenhui, College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract

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 improved 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.

Keywords

roadway section, fracture detection, DC-UNet model, model lightweight nature, image recognition, semantic segmentation, coal mine

DOI

10.12363/issn.1001-1986.24.08.0509

Recommended Citation

DONG Zheng, ZHANG Xuhui, YANG Wenjuan, et al. (2024) "A DC-UNet-based image processing method for detecting fractures along roadway sections of coal mines," Coal Geology & Exploration: Vol. 52: Iss. 12, Article 21.
DOI: 10.12363/issn.1001-1986.24.08.0509
Available at: https://cge.researchcommons.org/journal/vol52/iss12/21

Reference

[1] 王国法，庞义辉，任怀伟，等. 智慧矿山系统工程及关键技术研究与实践[J]. 煤炭学报，2024，49(1)：181−202.

WANG Guofa，PANG Yihui，REN Huaiwei，et al. System engineering and key technologies research and practice of smart mine[J]. Journal of China Coal Society，2024，49(1)：181−202.

[2] 王虹，王步康，张小峰，等. 煤矿智能快掘关键技术与工程实践[J]. 煤炭学报，2021，46(7)：2068−2083.

WANG Hong，WANG Bukang，ZHANG Xiaofeng，et al. Key technology and engineering practice of intelligent rapid heading in coal mine[J]. Journal of China Coal Society，2021，46(7)：2068−2083.

[3] 田伟琴，田原，贾曲，等. 悬臂式掘进机导航技术研究现状及发展趋势[J]. 煤炭科学技术，2022，50(3)：267−274.

TIAN Weiqin，TIAN Yuan，JIA Qu，et al. Research status and development trend of cantilever roadheader navigation technology[J]. Coal Science and Technology，2022，50(3)：267−274.

[4] 夏彬伟，廖传斌，罗亚飞，等. 基于分形理论的煤岩裂隙网络渗透率模型[J]. 煤田地质与勘探，2023，51(8)：107−115.

XIA Binwei，LIAO Chuanbin，LUO Yafei，et al. Fractal theory-based permeability model of fracture networks in coals[J]. Coal Geology & Exploration，2023，51(8)：107−115.

[5] 高辉，吴振坤，柯雨，等. 基于自然图像的钻探岩心识别[J]. 煤田地质与勘探，2023，51(9)：64−71.

GAO Hui，WU Zhenkun，KE Yu，et al. Drilling core identification based on natural image[J]. Coal Geology & Exploration，2023，51(9)：64−71.

[6] 高常鑫，徐正泽，吴东岳，等. 深度学习实时语义分割综述[J]. 中国图象图形学报，2024，29(5)：1119−1145.

GAO Changxin，XU Zhengze，WU Dongyue，et al. Deep learning-based real-time semantic segmentation：A survey[J]. Journal of Image and Graphics，2024，29(5)：1119−1145.

[7] JIANG Feng，WANG Gang，HE Peng，et al. Application of canny operator threshold adaptive segmentation algorithm combined with digital image processing in tunnel face crevice extraction[J]. The Journal of Supercomputing，2022，78(9)：11601−11620.

[8] 王泰华，董征，刘加奇，等. 基于手机图像处理的煤炭GSI检测系统设计与实现[J]. 煤炭科学技术，2021，49(3)：154−159.

WANG Taihua，DONG Zheng，LIU Jiaqi，et al. Design and complete of coal GSI detection system based on mobile image processing[J]. Coal Science and Technology，2021，49(3)：154−159.

[9] 詹伟，章杨松. 基于图像处理的岩体结构面迹线半自动检测[J]. 岩土工程技术，2020，34(1)：7−12.

ZHAN Wei，ZHANG Yangsong. Semi-automatic detection of rock mass discontinuity trace based on image processing[J]. Geotechnical Engineering Technique，2020，34(1)：7−12.

[10] 邓兆鹏，曹茂永，LAXMISHA Rai. 基于改进的区域生长和Hough变换的钻孔图像水平裂隙识别[J]. 科学技术与工程，2019，19(31)：239−245.

DENG Zhaopeng，CAO Maoyong，LAXMISHA Rai. Horizontal fracture recognition of borehole image based on improved region growing and Hough transform[J]. Science Technology and Engineering，2019，19(31)：239−245.

[11] 张农，袁钰鑫，韩昌良，等. 基于Mask R-CNN的煤矿巷道掘进迎头裂隙检测与定位算法[J]. 采矿与安全工程学报，2023，40(5)：925−932.

ZHANG Nong，YUAN Yuxin，HAN Changliang，et al. Research on crack detection and localization algorithm for advancing face in coalmine roadways based on Mask R-CNN[J]. Journal of Mining & Safety Engineering，2023，40(5)：925−932.

[12] 景莹，阮顺领，卢才武，等. 基于改进Mask R-CNN的露天矿边坡裂隙智能检测算法[J]. 重庆大学学报，2023，46(2)：67−80.

JING Ying，RUAN Shunling，LU Caiwu，et al. An intelligent detection method for open-pit slope fracture based on the improved Mask R-CNN[J]. Journal of Chongqing University，2023，46(2)：67−80.

[13] LEI Mingfeng，ZHANG Yunbo，DENG E，et al. Intelligent recognition of joints and fissures in tunnel faces using an improved mask region-based convolutional neural network algorithm[J]. Computer-Aided Civil and Infrastructure Engineering，2024，39(8)：1123−1142.

[14] 黄宏伟，陈佳耀. 基于机器视觉的隧道围岩智能识别分级与开挖安全风险研究[J]. 应用基础与工程科学学报，2023，31(6)：1382−1409.

HUANG Hongwei，CHEN Jiayao. Machine vision-based study on intelligent rating and excavation safety risk assessment of rock tunnel[J]. Journal of Basic Science and Engineering，2023，31(6)：1382−1409.

[15] CHEN Jiayao，ZHOU Mingliang，HUANG Hongwei，et al. Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning[J]. International Journal of Rock Mechanics and Mining Sciences，2021，142：104745.

[16] 罗虎，Miller Mark，张睿，等. 基于计算机视觉技术和深度学习的隧道掌子面岩体裂隙自动识别方法研究[J]. 现代隧道技术，2023，60(1)：56−65.

LUO Hu，Miller Mark，ZHANG Rui，et al. Research on the automatic identification method for rock mass fracture in tunnel face based on computer vision technology and deep learning[J]. Modern Tunnelling Technology，2023，60(1)：56−65.

[17] YANG Qiaoning，JI Xiaodong. Automatic pixel-level crack detection for civil infrastructure using unet and deep transfer learning[J]. IEEE Sensors Journal，2021，21(17)：19165−19175.

[18] XU Jinjian，ZHANG Hao，TANG Chaosheng，et al. Automatic soil crack recognition under uneven illumination condition with the application of artificial intelligence[J]. Engineering Geology，2022，296：106495.

[19] 何源宏，姜晶菲，许金伟. 注意力机制量化剪枝优化方法[J]. 国防科技大学学报，2024，46(1)：113−120.

HE Yuanhong，JIANG Jingfei，XU Jinwei. Quantization and pruning optimization method for attention mechanism[J]. Journal of National University of Defense Technology，2024，46(1)：113−120.

[20] 张凯兵，马东佟，孟雅蕾. 基于双源自适应知识蒸馏的轻量化图像分类方法[J]. 西安工程大学学报，2023，37(4)：82−91.

ZHANG Kaibing，MA Dongtong，MENG Yalei. A lightweight image classification method based on dual-source adaptive knowledge distillation[J]. Journal of Xi’an Polytechnic University，2023，37(4)：82−91.

[21] 文国军，高晓峰，毛宇，等. 基于GRU-CNN网络的隧道裂缝实时检测算法[J]. 地质科技通报，2023，42(6)：249−256.

WEN Guojun，GAO Xiaofeng，MAO Yu，et al. Real-time detection algorithm of tunnel cracks based on GRU-CNN[J]. Bulletin of Geological Science and Technology，2023，42(6)：249−256.

[22] 高杨，曹仰杰，段鹏松. 神经网络模型轻量化方法综述[J]. 计算机科学，2024，51(增刊1)：23–33.

GAO Yang，CAO Yangjie，DUAN Pengsong. Summary of lightweight methods of neural network model[J]. Computer Science，2024，51(Sup.1)：23–33.

[23] 陈法法，董海飞，何向阳，等. 改进U_Net网络的钢结构表面锈蚀图像分割方法[J]. 电子测量与仪器学报，2024，38(2)：49−57.

CHEN Fafa，DONG Haifei，HE Xiangyang，et al. Improved steel structure surface rust image segmentation method for U_Net network[J]. Journal of Electronic Measurement and Instrumentation，2024，38(2)：49−57.

[24] 孟庆成，李明健，万达，等. 基于M-Unet的混凝土裂缝实时分割算法[J]. 土木与环境工程学报(中英文)，2024，46(1)：215−222.

MENG Qingcheng，LI Mingjian，WAN Da，et al. Real-time segmentation algorithm of concrete cracks based on M-Unet[J]. Journal of Civil and Environmental Engineering，2024，46(1)：215−222.

[25] MENG Qingcheng，HU Lei，WAN Da，et al. Image-based concrete cracks identification under complex background with lightweight convolutional neural network[J]. KSCE Journal of Civil Engineering，2023，27(12)：5231−5242.