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:2019,28(6):228-234
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基于深度学习的铁路图像场景分类优化研究
(1.中国铁道科学研究院 研究生部, 北京 100081;2.中国铁道科学研究院 铁路大数据研究与应用创新中心, 北京 100081)
Research on Optimization Method of Railway Image Scene Classification Based on Deep Learning Method
(1.Department of Postgraduates, China Academy of Railway Sciences, Beijing 100081, China;2.Railway Big Data Research Center, China Academy of Railway Sciences, Beijing 100081, China)
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投稿时间:2018-12-10    修订日期:2018-12-29
中文摘要: 铁路检测、监测领域产生海量的图像数据,基于图像场景进行分类对图像后续分析、管理具有重要价值.本文提出一种结合深度卷积神经神经网络DCNN (Deep Convolutional Neural Networks)与梯度类激活映射Grad-CAM (Grad Class Activation Mapping)的可视化场景分类模型,DCNN在铁路场景分类图像数据集进行迁移学习,实现特征提取,Grad-CAM根据梯度全局平均计算权重实现对类别的加权热力图及激活分数计算,提升分类模型可解释性.实验中对比了不同的DCNN网络结构对铁路图像场景分类任务性能影响,对场景分类模型实现可视化解释,基于可视化模型提出了通过降低数据集内部偏差提升模型分类能力的优化流程,验证了深度学习技术对于图像场景分类任务的有效性.
Abstract:The field of railway detection and monitoring generates massive image data, image scene classification is of great value for subsequent analysis and management. In this study, a visual scene classification model that combines Deep Convolutional Neural Networks (DCNN) and Grad Class Activation Mapping (Grad-CAM) is proposed, DCNN extract feature of railway scene classification image dataset by transfer learning method, Grad-CAM improves the interpretability of the classification model by calculating the weighted thermogram and activation scores of the categories. In the experiment, the effects of different DCNN structures on the performance of railway image scene classification tasks are compared, and visual interpretation of scene classification model is realized. At the same time, based on visualization method, an optimization process is proposed to improve model classification ability by reducing internal deviation of dataset, which verifies the effectiveness of the deep learning technology for image scene classification task.
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基金项目:铁科院院基金重大课题(2017YJ005)
引用文本:
赵冰,李平,代明睿,马小宁.基于深度学习的铁路图像场景分类优化研究.计算机系统应用,2019,28(6):228-234
ZHAO Bing,LI Ping,DAI Ming-Rui,MA Xiao-Ning.Research on Optimization Method of Railway Image Scene Classification Based on Deep Learning Method.COMPUTER SYSTEMS APPLICATIONS,2019,28(6):228-234

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