Inspection of exterior substance on high-speed train bottom based on improved deep learning method. (15th October 2020)

• Record Type: Journal Article
• Authors: Yao, Zikai; He, Deqiang; Chen, Yanjun; Liu, Bin; Miao, Jian; Deng, Jianxin; Shan, Sheng
• Is Part Of: Measurement. Volume 163(2020)
• Publication Date: 2020-10-15
• Subjects: Deep learning -- High-speed train -- Fault diagnosis -- Target recognition -- Feature extraction -- CNN; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108013
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A deep learning method for inspection of exterior substance is proposed. Dense connection and spatial pyramid pooling are used to extract features. Data augmentation and transfer learning was used to build inspection model. The proposed model significant outperformed other deep learning method. Abstract: When a high-speed train is running, exterior substances, such as rail-side plastic bags, are easily rolled into the bogies, cables and equipment gaps, which can easily cause smoke, odor and even equipment short-circuits and fires. Current inspection methods have many disadvantages. To overcome these defects, this paper presents a fast intelligent inspection model based on the YOLO V3 deep learning method for exterior substance inspection. We utilized the densely convolutional network as the feature extractor to enhances the feature propagation and ensure maximum information flow in the YOLO V3 method. Spatial pyramid pooling networks was used in the YOLO V3 method to reduce context information loss. The transfer learning and data augmentation technologies were applied to make the present method easier to train. Compared with other method, the proposed method shows satisfactory performance in terms of precision and recall, which is the stare-of-art exterior substance inspection method.