基于改进MobileNetV2的钻杆计数方法

Drill pipe counting method based on improved MobileNetV2

  • 摘要: 针对现有基于人工及仪器的钻杆计数法存在精度较低、耗时耗力,现有基于图像处理的钻杆计数方法难以提取图像特征,网络模型复杂度高、计算量大等问题,提出了一种基于改进MobileNetV2的钻杆计数方法。通过摄像头采集钻机工作状态图像,采用数据增强对采集的图像进行预处理,在MobileNetV2的基础上,添加卷积注意力模块增强特征的细化能力,优化目标函数提升识别精度,通过迁移学习获取初始参数。将改进后的MobileNetV2作为钻机工作状态识别模型,提取钻机工作状态特征,通过识别钻杆钻进完整过程中装钻杆、打钻杆、卸钻杆、停机4种钻机工作状态生成置信度数据,通过滑动窗口对置信度数据进行滤波,统计钻杆数量,明确钻孔深度。实验结果表明:改进后的MobileNetV2模型识别准确率达99.95%,与经典分类模型ResNet50,Xception,InceptionV3,InceptionResNetV2,MobileNetV2相比,准确率分别提升了1.35%,1.28%,1.43%,0.85%,1.25%,参数量比MobileNetV2模型减少了38.9%,模型收敛速度更快,综合性能更好。将基于改进MobileNetV2的钻杆计数方法应用于煤矿综采工作面的钻杆计数中,平均钻杆计数精度为98.4%,实现了钻杆精确计数,验证了该方法在复杂环境下应用的可行性和实用性。

     

    Abstract: The existing drill pipe counting methods based on manual and instrument have the problems of low precision, time-consuming and labor-consuming. The existing drill pipe counting methods based on image processing are difficult to extract image features, the network model has high complexity and large amount of computation. In order to solve the above problems, a drill pipe counting method based on improved MobileNetV2 is proposed. The working state image of the drilling rig is collected through a camera. The collected image is preprocessed by adopting data enhancement. On the basis of MobileNetV2, the convolutional block attention module is added to enhance the thinning capability of features. The objective function is optimized to improve the recognition precision. The initial parameters are obtained through transfer learning. The improved MobileNetV2 is used as the working state recognition model of the drilling rig. The working state features of the drilling rig are extracted by the model. The confidence data are generated by recognizing the four working states of the drilling rig, including drill pipe installation, drill pipe driving, drill pipe unloading and shut down during the whole drilling process of the drill pipe. The confidence data are filtered through a sliding window. The number of drill pipes is accurately counted, and the drilling depth is determined. The experimental results show that the recognition accuracy of the improved MobileNetV2 model reaches 99.95%. Compared with the classical classification models ResNet50, Xception, InceptionV3, InceptionResNetV2 and MobileNetV2, the accuracy is improved by 1.35%, 1.28%, 1.43%, 0.85% and 1.25% respectively. The parameter is reduced by 38.9% compared with the MobileNetV2 model. The convergence speed of the model is faster and the comprehensive performance is better. The drill pipe counting method based on the improved MobileNetV2 is applied to the drill pipe counting of fully mechanized mining face of a coal mine. The average drill pipe statistical precision is 98.4%. The accurate counting of the drill pipes is realized. The feasibility and practicability of application of the method in the complex environment are verified.

     

/

返回文章
返回