随着煤炭分选行业对智能化干分选煤技术需求和煤矸图像识别方法需求的增长,研究煤矿复杂分选条件下煤矸混合特征图像的识别方法显得愈发重要。依据深度学习、图像识别和无线通信等理论,设计基于卷积神经网络的煤矸识别定位系统。根据煤矿分选过程的复杂条件,分析煤矸表面特征的5种状态类别,构建煤矸数据集。基于迁移学习的改进AlexNet网络和RPN网络获取煤矸混合特征图像样本的分类信息和像素坐标,通过相机标定方法获得像素坐标在相机坐标系中的位置坐标。构建煤矸分拣机器人分布式控制系统的局域网络,实现识别定位系统与主控系统的实时煤矸检测信息交互。基于煤矸识别定位系统对煤矸图像的检测模型进行测试,试验结果表明,煤矸识别定位系统的识别模型检测准确率可达90.17%,煤矸目标最大定位误差9.45 mm,系统响应测试时间低于350 ms,满足煤矿复杂分选的基本要求。该煤矸识别模型对煤矸混合特征图像具有较好的检测结果,为煤矸图像识别方法应用于煤矿智能化分选发展提供了研究基础。

As the coal washing industry’s demand for intelligent dry coal preparation technology and coal gangue image recognition methods grows, it is becoming more and more important to study the recognition methods of coal gangue mixed feature images under complex coal washing conditions. Based on theories of deep learning, image recognition and wireless communication, a coal gangue recognition and location system was designed based on convolutional neural networks in this paper. According to the complex conditions of the coal mine washing process, the five state categories of the surface characteristics of coal gangue were analyzed to construct a gangue data set. The improved AlexNet network and RPN network based on transfer learning obtain the classification information and pixel coordinates of the gangue mixed feature image samples, and obtain the position coordinates of the pixel coordinates in the camera coordinate system through the camera calibration method. The local area network of the coal gangue sorting robot distributed control system was constructed to realize the real-time coal gangue detection information interaction between the identification and positioning system and the main control system. The detection model of coal gangue image wass tested based on the coal gangue recognition and positioning system. The test results show that the detection accuracy of the recognition model of the gangue recognition and positioning system can reach 90.17%, the maximum positioning error of the gangue target is 9.45 mm, and the system response time is less than 350 ms, which meets the basic requirements of the complex washing of coal mines. The coal gangue recognition model has good detection results on the mixed feature images of coal gangue, which provides a research foundation for the application of coal gangue image recognition method to the development of intelligent coal preparation.

0 引言

1 煤矸识别定位流程与算法

   1.1 煤矸识别定位系统组成

   1.2 煤矸图像表面特征分析

   1.3 煤矸图像识别方法

   1.4 煤矸定位方法

2 数据集制作及模型训练

   2.1 图像采集及数据集构建

   2.2 模型训练

3 煤矸识别定位系统试验

   3.1 煤矸识别试验

   3.2 定位试验

   3.3 识别定位系统测试

   3.4 识别定位系统的带速匹配试验

4 结论