Coal-rock image recognition method integrating drilling geological information
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摘要: 当前应用于煤岩图像识别的深度卷积神经网络模型存在体积庞大、计算过程冗杂等问题，难以满足实时检测要求，且对低照度、高粉尘等复杂环境适应性差。针对上述问题，提出了一种融合钻孔地质信息的煤岩图像识别方法。首先，通过改进的谱残差显著性检测（ISRSD）算法增强煤岩图像质量，有效减弱复杂环境对煤岩图像特征造成的不利影响；然后，使用加入注意力机制的VGG（AVGG）深度卷积神经网络模型——在VGG的基础上进行剪枝、加入卷积注意力模块（CBAM）和引入自适应学习率调整策略，高效提取煤岩图像特征；最后，利用贝叶斯模型融合煤岩图像特征和由钻孔地质柱状图获取的钻孔地质信息，提升煤岩分类的准确性和鲁棒性。实验结果表明，经ISRSD算法增强后的图像目标更突出，色彩失真程度更低，且边缘、纹理等图像特征保留相对完整； AVGG模型的准确率与VGG模型相当，但平均推理时间、参数量及模型大小分别仅为VGG模型的15.61%，33.44%及33.40%；与仅使用AVGG模型识别煤岩图像相比，利用贝叶斯模型融合钻孔地质信息后，准确率提高了1.85%，达97.31%。Abstract: The current deep convolutional neural network models applied to coal-rock image recognition have problems such as large volume and cumbersome calculation process. It is difficult to meet real-time detection requirements, and it has poor adaptability to complex environments such as low lighting and high dust. In order to solve the above problems, a coal-rock image recognition method integrating drilling geological information is proposed. Firstly, the improved spectral residual saliency detection (ISRSD) algorithm is used to enhance the quality of coal-rock images, effectively reducing the adverse effects of complex environments on the features of coal-rock images. Secondly, the method uses the attentional VGG (AVGG) deep convolutional neural network model. The AVGG performs pruning based on VGG, adds convolutional block attention module (CBAM), and introduces adaptive learning rate adjustment strategy to efficiently extract coal-rock image features. Finally, the Bayesian model is used to integrate the features of coal-rock images with the geological information obtained from the borehole geological column chart, in order to improve the accuracy and robustness of coal-rock classification. The experimental results show that the image enhanced by the ISRSD algorithm has more prominent targets, lower color distortion, and relatively complete preservation of image features such as edges and textures. The accuracy of the AVGG model is comparable to that of the VGG model, but the average inference time, parameter count, and model size are only 15.61%, 33.44%, and 33.40% of the VGG model, respectively. Compared with using only the AVGG model to recognize coal-rock images, using the Bayesian model to fuse drilling geological information improves accuracy by 1.85%, reaching 97.31%.
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图 1 融合钻孔地质信息的煤岩图像识别方法流程
Figure 1. Process of coal-rock image recognition method integrating drilling geological information
图 2 ISRSD算法流程
Figure 2. Improved spectral residual saliency detection（ISRSD） algorithm flow
图 6 不同显著性检测算法增强效果对比
Figure 6. Comparison of enhancement effects of different saliency detection algorithms
图 7 不同显著性检测算法下AVGG模型的准确率
Figure 7. Accuracy of attentional visual geometry group（AVGG） model under different saliency detection algorithms
图 8 融合钻孔地质信息前后煤岩识别准确率对比
Figure 8. Comparison of coal-rock recognition accuracy before and after fusion borehole geological information
表 1 数据集扩充及类别均衡前后样本数量
Table 1. Sample size before and after data set augmentation and category balancing
类别 数据集扩充及类别
均衡前样本数/张数据集扩充及类别
均衡后样本数/张砂岩 337 1 515 砾岩 96 1 344 泥岩 369 1 348 煤炭 360 1 440 页岩 82 1 394 
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表 2 不同模型评价指标结果对比
Table 2. Comparison of evaluation index results of different models
模型 准确率/% 单个样本
平均推理时间/ms参数量/106个 模型大小
/MiBGoogLeNet 89.65 68.49 5.981 24 VGG 93.79 117.65 128.810 515 SVGG 83.72 18.86 43.081 172 AVGG 91.33 18.36 43.084 172
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表 3 不同显著性检测算法耗时对比
Table 3. Comparison of time consumption of different saliency detection algorithms
算法 单个样本耗时/ms ISRSD 7.9 FT 5.5 LC 15.9 HC 194.3
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