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Adaptive Deep Learning for High-Speed Railway Catenary Swivel Clevis Defects Detection
- Source :
- IEEE Transactions on Intelligent Transportation Systems. 23:1299-1310
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- The swivel clevis (SC) is a vulnerable part of the Overhead Catenary System (OCS). Regular inspection using computer vision technology is an effective way to detect SC defects and improve the OCS operation safety. However, achieving full automation of SC defects detection is still a difficult task due to defective sample scarcity and data distribution shift. To overcome these problems, this paper proposes a novel defects detection method that combines an adaptive SC segmentation network (Adaptive SSN) and local operators. During the inspection process, an unreliability index defined by the model uncertainty and prior knowledge is used to monitor the reliability of the Adaptive SSN. When data distribution shift causes the Adaptive SSN to be unreliable, human annotator will be asked to update the training set and retrain the Adaptive SSN to adapt to the new data distribution. Then the geometric features obtained from segmentation masks and the local features extracted by local operators are used to detect the SC defects. Effectiveness of the proposed method is demonstrated by the experimental results on the data from several high-speed railway lines.
- Subjects :
- Computer science
business.industry
Mechanical Engineering
Reliability (computer networking)
Deep learning
Real-time computing
Process (computing)
Sample (statistics)
Automation
Computer Science Applications
Automotive Engineering
Catenary
Overhead (computing)
Segmentation
Artificial intelligence
business
Subjects
Details
- ISSN :
- 15580016 and 15249050
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Accession number :
- edsair.doi...........b8267800ebf7d4b3c8a2af9bb1dc75fa