针对小样本数据难以构建深度学习模型和实际工况下多尺度形态、颜色煤矸的识别率低的问题,提出了一种融合迁移学习思想与结构优化的煤矸深度识别模型的优化方法。模仿井下实际生产环境搭建机器视觉平台,采用CCD（Charge Couplect Device)工业相机实时获取煤和矸石图像,利用图像旋转、翻转以及增加噪声方式扩展煤和矸石数据集的多样性。从降低模型训练时间出发,提出一种迁移权重和简化神经元(Transfer Weight-Reduce Neurons, TW-RN)模型优化方法改进预训练卷积神经网络（Convolutional Neural Network,CNN)模型,构建了改进后的Im_AlexNet、Im_VGG16、Im_VGG19、Im_ResNet50四种煤矸识别模型。依托扩充后的煤和矸石数据集,仿真对比了4种模型在不同优化器类型、学习率设置方式下的训练结果,确定了每种模型的最佳优化器类型和学习率设置方式。以测试准确率、F1分数、模型内存大小、训练时间4种评估参数为基准,定量评价改进前后每种模型的性能,确定了基于TW-RN优化CNN的最佳煤矸识别网络模型。结果表明:基于TW-RN改进的4种煤矸识别模型的识别准确率均得到了有效提高,且模型训练时间、内存大小均显著降低。煤矸识别率与模型复杂度的关系呈非正相关,相比改进后的Im_VGG16、Im_VGG19和Im_ResNet50深度识别模型, TW-RN方法改进的浅层Im_AlexNet模型性能得到显著提升,其识别精度提高了2.149个百分点,达97.461%,占用内存降低了190 MB,单张图像的识别时间节省了0.788 ms。

In view of the problem that it is difficult to construct a deep learning model with small sample data and the low recognition rate of multi-scale morphology and color coal gangue under actual working conditions, an optimization method of coal gangue depth recognition model combining migration learning ideas and structural optimization is proposed. A machine vision platform was built by imitating the actual production environment of underground mines, and the CCD industrial camera was used to obtain images of coal and gangue in real-time, the diversity of coal and gangue data sets were increased by using of image rotation, inversion and noise increase. In order to reduce the cost of model training time, a Transfer Weight-Reduce Neurons (TW-RN) model optimization method was proposed to improve the pre-training CNN model, and four kinds of improved model：Im_AlexNet, Im_VGG16, Im_VGG19 and Im_ResNet50 were constructed.Relying on the expanded coal and gangue data sets, this simulation test compared the training results of four models under different optimizer types and setting methods of learning rates, and determined the best optimizer type and learning rate setting method for each model. Based on the four evaluation parameters of test accuracy, F1 score, model memory size and training time, the performance of each model before and after the improvement was quantitatively evaluated, and an optimal coal and gangue recognition network model based on TW-RN optimized CNN was determined. The results show that the recognition accuracy of the four coal and gangue recognition models based on TW-RN has been effectively improved, and the training time and the memory size of the model have been significantly reduced. The coal gangue recognition rate has a non-positive correlation with the model complexity. Compared with the improved Im_VGG16, Im_VGG19 and Im_ResNet50 depth recognition models, the performance of the shallow Im_AlexNet model improved by the TW-RN method has been significantly improved, and its recognition accuracy has increased by 2.149 percentage points, up to 97.461%, the memory consumption is reduced by 190MB. The recognition time of a single image is saved by 0.788 milliseconds.

0 引言

1 理论基础

   1.1 卷积神经网络

   1.2 深度迁移学习

2 基于TW-RN的煤矸识别

   2.1 TW-RN模型优化方法

   2.2 煤矸识别模型的构建

   2.3 煤矸识别仿真试验设计

3 试验平台设计

   3.1 数据集采集

   3.2 数据集预处理

4 试验结果与分析

   4.1 评估参数

   4.2 模型超参数确定

   4.3 模型效果分析

5 结论