Coal and gangue image classification model based on improved feedback neural network
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摘要: 现有的基于深度学习的图像分类方法存在分类模型参数量大、耗时长、分类精度低，难以在模型轻便和分类精度上达到折衷。针对上述问题，提出了一种基于改进反馈神经网络（Feedback−Net）的煤矸石图像分类模型。在ResNet50模型的基础上搭建Feedback−Net模型，通过在ResNet50模型搭建反馈通路，将高低阶信息进行融合，从而提升特征的表现能力。针对搭建的Feedback−Net模型在分类准确率提升的同时耗时有所增加的问题，将Feedback−Net模型中的方形卷积核改进为非对称卷积块（ACB），通过叠加融合的方式增加卷积核的特征提取能力；将参数量最多的全连接层转换为全局协方差池化（GCP），以降低网络参数量。通过模拟煤矿井下煤矸石分拣环境，以验证Feedback−Net模型和改进Feedback−Net模型（Feedback−Net+ACB和Feedback−Net+ACB+GCP）的性能。结果表明：① Feedback−Net模型在精度上高于ResNet50模型，损失值略低于ResNet50模型。② Feedback−Net模型训练精度较ResNet50模型提升了1.20%，测试精度提升了1.21%，但训练耗时较ResNet50模型增加了0.22%。③ Feedback−Net+ACB+GCP模型精度高于Feedback−Net和Feedback−Net+ACB模型，其收敛速度在3个模型中最快，具有最优性能。④ Feedback−Net+ACB模型测试精度、训练精度均较Feedback−Net模型提升了1.39%，且耗时减少了15.53 min，Feedback−Net+ACB+GCP模型训练精度、测试精度较Feedback−Net模型分别提升了1.62%，1.59%，耗时缩短了1.12%；Feedback−Net+ACB+GCP模型耗时较Feedback−Net+ACB模型减少了50.38 min，性能更加优越。Abstract: The existing image classification methods based on deep learning have the problems of the large number of classification model parameters, long time consumption and low classification precision. It is difficult to achieve a compromise between the portability of the model and the classification precision. In order to solve the above problems, a coal and gangue image classification model based on improved Feedback-Net is proposed. The Feedback-Net model is built on the basis of the ResNet50 model. The high-order information and the low-order information are fused by building a feedback path in the ResNet50 model. Therefore, the representation capability of the features is improved. The constructed Feedback-Net model consumes more time while improving the classification accuracy. In order to solve this problem, the square convolution block in the Feedback-Net model is improved into an asymmetric convolution block (ACB). The feature extraction capability of the convolution kernel is increased by superposition and fusion. The full connection layer with the largest number of parameters is replaced by global covariance pooling (GCP) to reduce the number of network parameters. By simulating the environment of coal and gangue sorting in coal mines, the performance of the Feedback-Net model and the improved Feedback-Net model (Feedback-Net + ACB and Feedback-Net + ACB + GCP) is verified. The results show the following points. ① The precision of the Feedback-Net model is higher than that of the ResNet50 model, and the loss value is slightly lower than that of the ResNet50 model. ② Compared with the ResNet50 model, the training precision of the Feedback-Net model is improved by 1.20%. The testing precision is improved by 1.21%, but the training time is increased by 0.22%. ③ The precision of the Feedback-Net + ACB + GCP model is high than that of the Feedback-Net and Feedback-Net + ACB model. The Feedback-Net + ACB + GCP model's convergence rate is the fastest among the three models. It has the best performance. ④ Compared with the Feedback-Net model, the testing precision and training precision of the Feedback-Net + ACB model are improved by 1.39%. The time consumption is reduced by 15.53 minutes. Compared with the Feedback-Net model, the training precision and testing precision of the Feedback-Net + ACB + GCP model are improved by 1.62% and 1.59% respectively. The time consumption is reduced by 1.12%. Compared with the Feedback-Net+ACB model, the time consumption of the Feedback-Net+ACB+GCP model is reduced by 50.38 minutes. The performance of the Feed-Net+ACB+GCP model is better.
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图 8 Feedback−Net模型中不同卷积层级特征提取
Figure 8. Feature extraction of different convolution levelsin the Feedback-Net model
图 9 Feedback−Net模型与ResNet50模型训练精度收敛过程
Figure 9. Training precision convergence process of the feedback-Net model and the ResNet50 model
图 10 Feedback−Net模型与ResNet50模型的损失值收敛过程
Figure 10. The Loss value convergence process of the Feedback-Net model and the ResNet50 model
表 1 Feedback−Net模型与ResNet50模型性能对比
Table 1. Performance comparison between the Feedback-Net model and the ResNet50 model
模型 评价指标 训练耗时/min 训练精度 测试精度 ResNet50 9 963.42 0.934 5 0.934 5 Feedback−Net 9 985.35 0.945 7 0.945 7 
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表 2 各模型性能对比
Table 2. Performance comparison of each model
模型 评价指标 训练耗时/min 训练精度 测试精度 Feedback−Net 5 869.73 0.962 8 0.963 1 Feedback−Net+ACB 5 854.20 0.976 2 0.976 5 Feedback−Net+ACB+GCP 5803.82 0.978 4 0.978 4
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