为了提高煤岩识别的精准度, 采集了内蒙古上海庙矿业有限责任公司榆树井煤矿掘进工作面煤岩原始图像并制作了深度学习数据集, 通过FCN全卷积神经网络(FCN网络)、U-net语义分割网络(U-net网络)与加入Canny边缘检测算法改进后的U-net网络等3种网络模型对数据集进行训练, 并对训练结果进行对比分析。分析结果表明: 在训练次数达到100次时, 3种网络模型准确率分别为89.25%, 93.52%及94.55%, 改进U-net网络模型准确率相较改进前提高1.03%; 在煤岩识别方面, U-net网络模型比FCN网络模型取得了更高的准确率, 在测试环节中也表现出了更好的性能; 在预测环节中, 对煤岩边缘部分的识别做到了更为精准的处理。该方法可为煤岩识别的精准度的提高提供参考。

To improve the accuracy of coal rock recognition, this study collected the original images of coal rock from the excavation face in Yushujing coal mine of Shanghai Temple Mining Co. Inner Mongolia, and produced a deep learning dataset. The dataset is trained by three kinds of network models, including FCN fully convolutional neural network (FCN network), U-net Semantic Segmentation Network (U-net Network), and U-net Network improved by adding Canny Edge Detection Algorithm, and the training results were compared and analyzed. The results show that the accuracy of the three network models is 89.25%, 93.52% and 94.55%, respectively. When the number of training times reaches 100, the accuracy of the improved U-net network model increased by 1.03%. In coal rock identification, the U-net network model achieved higher accuracy than the FCN network model and showed better performance in the testing session. In the prediction session, the recognition of the edge part of the coal rock was achieved with more accurate treatment. The method can provide a reference for improvement of the accuracy of coal rock recognition.