School of Mechanical Engineering, Liaoning Technical University, Fuxin, China.
Liaoning Provincial Key Laboratory of Large-Scale Mining Equipment, Fuxin, China.
PLoS One. 2023 Feb 9;18(2):e0281397. doi: 10.1371/journal.pone.0281397. eCollection 2023.
Aiming at the problems of low accuracy of coal gangue recognition and difficult recognition of mixed gangue rate, a coal rock recognition method based on modal fusion of RGB and infrared is proposed. A fully mechanized coal gangue transportation test bed is built, RGB images are obtained by camera, and infrared images are obtained by industrial microwave heating system and infrared thermal imager. the image data of the whole coal, whole gangue, and coal gangue with different gangue mixing as training and test samples, identify the released coal gangue and its mixing rate. The AlexNet, VGG-16, ResNet-18 classification networks and their convolutional neural networks with modal feature fusion are constructed. results: The classification accuracy of ResNet networks on RGB and infrared image data is higher than AlexNet and VGG-16 networks. The early convergence network performance of ResNet is verified through the convergence of different models. The recognition rate of the network is 97.92 the confusion matrix statistics, which verifies the feasibility of the application of modal fusion method in the field of coal gangue recognition. The fusion of modal features and early models of ResNet coal gangue, which is the basic premise for realizing intelligent coal caving.
针对煤矸石识别准确率低和混合矸石率识别困难的问题，提出了一种基于 RGB 和红外模态融合的煤岩识别方法。搭建了综采矸石运输试验台，利用相机获取 RGB 图像，利用工业微波加热系统和红外热像仪获取红外图像。以全煤、全矸和不同矸石混合的矸石作为训练和测试样本的图像数据，识别释放的煤矸石及其混合率。构建了 AlexNet、VGG-16、ResNet-18 分类网络及其模态特征融合的卷积神经网络。结果：ResNet 网络在 RGB 和红外图像数据上的分类准确率高于 AlexNet 和 VGG-16 网络。通过不同模型的收敛验证了 ResNet 早期收敛网络的性能。网络的识别率为 97.92，混淆矩阵统计验证了模态融合方法在煤矸石识别领域的应用可行性。ResNet 煤矸石模态特征融合和早期模型是实现智能化放顶煤的基本前提。
