基于改进卷积神经网络的铁路轨道线识别提取
陈文1季国一1邹劲柏1张立东2乔彦涵1
Railway Track Identification and Extraction Based on Improved Convolutional Neural Network
CHEN Wen1JI Guoyi1ZOU Jinbai1ZHANG Lidong2QIAO Yanhan1
-
作者信息:1.上海应用技术大学轨道交通学院，201418，上海
2.上海申通地铁集团有限公司技术中心，200233，上海
-
Affiliation:1.School of Railway Transportation, Shanghai Institute of Technology, 201418, Shanghai, China
2.Technical Center of Shanghai Shentong Metro Co., Ltd., 200233, Shanghai, China
-
关键词:
-
Key words:
-
DOI:10.16037/j.1007-869x.2024.09.049
-
中图分类号/CLCN:TP183;TP391.41;U216.3
-
栏目/Col:应用技术
摘要:
［目的］铁路异物入侵防护成为热点研究话题，而入侵区域的划分需要对轨道位置进行检测。为了确定图像中的轨道位置，提出了基于改进MaskR-CNN(掩码-区域卷积神经网络)结合数学模型的方法，对铁路轨道线进行识别提取。［方法］该方法先对MaskR-CNN进行优化并添加注意力机制，引入迁移学习提高模型泛化能力，再使用优化模型对轨道线进行识别与分割，然后提取分割数据并使用对应的数学表达式进行拟合，从而实现轨道线的识别提取。将图像中的轨道线分为四类，使用Labelme标注软件制作标签，形成训练集与测试集。使用训练集对优化模型进行训练，使用测试集评估优化模型的检测效果。［结果及结论］研究结果表明，相同训练力度下，该方法相较于其他分割模型及改进前模型表现优异，对于轨道线种类的判断准确率达97.5%，分割准确率也基本在80%以上。试验表明，该方法利用神经网络良好的表现力提高检测的普适性，能准确判断轨道类型并分割轨道。
Abstracts:
［Objective］ Prevention and protection of foreign objects intrusion into railway becomes a hot research topic, and the division of intrusion areas requires detection of the track location. In order to determine the track location in the image, a method based on improved Mask R-CNN (Mask region convolutional neural network) combined with mathematical model is proposed to identify and extract the railway track. ［Method］ Firstly, Mask R-CNN is optimized and added with attention mechanism, and transfer learning is introduced into the above method to improve the model′s generalization ability. Then the optimized model is used to identify and segment the track, extracting the segmentation data and fitting with the corresponding mathematical expression to realize the identification and extraction of the track. The track in the image is divided into four categories and labeled with the Labelme labeling software, forming a training set and a test set. The training set is used to train the optimized model, and the test set to evaluate the detection results of the optimized model. ［Result & Conclusion］ The research results show that compared with other segmenting models and the original model, the proposed method performs well under the same training intensity, reaching 97.5% of the accuracy rate in track type determination, and basically above 80% in track segmentation. Tests show that the proposed method improves the general applicability of the detection by using the good performance of neural network, and can accurately determine the track type and segment the track.
- 上一篇: 高铁物流运输模式及其可行性
- 下一篇: 数字化转型难在转型
过刊浏览
资料下载
友情链接