Segmentation method of the abnormal area of coal infrared thermal image
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摘要: 红外辐射可反映煤岩受载破坏情况，用于监测和预防煤岩动力灾害，但红外热像仪生成的红外热像图像素分辨率低、噪声较大，导致检测结果受主观因素影响较大，无法准确识别煤体损伤区域。将深度学习和红外热像结合进行无损检测已成为趋势，但目前结合深度学习和红外热像对煤体受载破坏进行识别检测的研究相对较少。针对上述问题，提出一种基于多尺度通道注意力模块(MS−CAM)U−Net模型的煤体红外热像异常区域分割方法。在传统U−Net模型的编码器中引入MS−CAM，设计了基于MS−CAM的U−Net模型结构，使模型在关注煤体红外热像异常区域显著特征的同时，还关注异常区域小目标特征，以提高异常区域分割精度。为降低煤体红外热像数据集匮乏对模型准确率和适用性的影响，对创建的煤体红外热像数据集进行数据增强操作，并采用MS COCO数据集对基于MS−CAM的U−Net模型进行预训练，再采用煤体红外热像数据集训练，得出最终网络权重。实验结果表明，该方法可有效分割煤体红外热像异常区域，精确率、F1分数、Dice系数和平均交并比分别为94.75%，94.94%，94.65%，90.03%，均优于Deeplab模型、U−Net模型和基于SENet注意力机制的U−Net模型。Abstract: Infrared radiation can reflect the damage of coal and rock under load, and can be used to monitor and prevent the dynamic disaster of coal and rock. But the infrared thermal image generated by the infrared thermal imager has low pixel resolution and large noise, which leads to the detection result being greatly affected by subjective factors. Therefore, the damaged area of the coal body cannot be accurately identified. It has become a trend to combine deep learning with infrared thermal imaging for nondestructive testing. But the research on the identification and detection of coal damage under load by combining deep learning and infrared thermal imaging is relatively few. In order to solve the above problems, a segmentation method of the abnormal area of coal infrared thermal image based on multi-scale channel attention module (MS-CAM) U-Net model is proposed. The MS-CAM is introduced into the encoder of the traditional U-Net model, and the U-Net model structure based on MS-CAM is designed. The model not only pays attention to the major characteristics of the coal infrared thermal image abnormal area, but also pays attention to the small target characteristics of the abnormal area, so as to improve the segmentation accuracy of the abnormal area. In order to reduce the influence of the lack of coal infrared thermal image data set on the accuracy and applicability of the model, the data enhancement operation is carried out on the created coal infrared thermal image data set. The MS-CAM-based U-Net model is pre-trained by using the MS COCO data set. Then the coal infrared thermal image data set is used for training to obtain the final network weight. The experimental result shows that the method can effectively segment the abnormal areas of the infrared thermal image of the coal body. The accuracy rate, the F1 score, the Dice coefficient and the average cross-combination ratio are 94.75%, 94.94%, 94.65%, and 90. 03% respectively. The results are superior to the Deeplab model, the U-Net model and the U-Net model based on the attention mechanism of the SENet.
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图 1 基于MS−CAM的U−Net模型结构
Figure 1. U-Net model structure based on multi-scale channel attention module(MS-CAM)
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图 4 煤样3种加载破坏时期红外热像图
Figure 4. Three infrared thermal images of coal samples during loading pressure period
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图 5 LabelMe工具中煤样红外热像异常区域标注
Figure 5. Abnormal area tagging in infrared thermal images of coal samples in LabelMe tool
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图 9 不同模型对煤体红外热像异常区域的分割结果
Figure 9. Segmentation results of infrared thermal images of coal samples by different models
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图 10 U−Net（SENet）模型和U−Net（MS−CAM）模型分割结果的类激活热力图
Figure 10. Class activation heat map of segmentation results by U-Net（SENet） model and U-Net（MS-CAM）model
表 1 基于MS−CAM的U−Net模型网络结构及对应特征图
Table 1 Network structures of U-Net model based on MS-CAM and corresponding characteristic images
网络结构 特征图尺寸 卷积核参数 Conv_1 256×256×64
256×256×643×3×64
3×3×64MS−CAM_1 256×256×64 − DownSampling_1 128×128×64 2×2 Conv_2 128×128×128
128×128×1283×3×128
3×3×128MS−CAM_2 128×128×128 − DownSampling_2 64×64×128 2×2 Conv_3 64×64×256
64×64×2563×3×256
3×3×256MS−CAM_3 64×64×256 − DownSampling_3 32×32×256 2×2 Conv_4 32×32×512
32×32×5123×3×512
3×3×512MS−CAM_4 32×32×512 − DownSampling_4 16×16×512 2×2 Conv 16×16×512
16×16×5123×3×512
3×3×512UpSampling_1 32×32×512 2×2 Concatenate_1 32×32×1024 UpSampling_1+ MS−CAM_4 conv_1 32×32×256
32×32×2563×3×256
3×3×256UpSampling_2 64×64×256 2×2 Concatenate_2 64×64×512 UpSampling_2+ MS−CAM_3 conv_2 64×64×128
64×64×1283×3×128
3×3×128UpSampling_3 128×128×128 2×2 Concatenate_3 128×128×256 UpSampling_3+ MS−CAM_2 conv_3 128×128×64
128×128×643×3×64
3×3×64UpSampling_4 256×256×64 2×2 Concatenate_4 256×256×128 UpSampling_4+ MS−CAM_1 conv_4 256×256×64
256×256×643×3×64
3×3×64Conv1×1 256×256×1 1×1×1 
下载: 导出CSV
表 2 不同模型分割结果评价指标对比
Table 2 Comparison of evaluation indexes for segmentation results of different models
% 模型 精确率 F1分数 Dice系数 MIoU Deeplab 88.65 90.31 87.92 83.78 U−Net 92.28 91.67 91.58 84.85 U−Net(SENet) 93.46 93.56 92.81 87.28 U−Net（MS−CAM） 94.75 94.94 94.65 90.03
下载: 导出CSV
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