Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data
AbstractRemote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Falque, R.; Vidal-Calleja, T.; Miro, J.V. Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data. Sensors 2017, 17, 2276.
Falque R, Vidal-Calleja T, Miro JV. Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data. Sensors. 2017; 17(10):2276.Chicago/Turabian Style
Falque, Raphael; Vidal-Calleja, Teresa; Miro, Jaime V. 2017. "Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data." Sensors 17, no. 10: 2276.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.