Defect detection for aluminium conductor composite core X-ray image with deep convolution network. (September 2020)

• Record Type: Journal Article
• Authors: Wei, Rui; Wei, Hanlai; Chen, Dabing; Xie, Lizhe; Wang, Zheng; Hu, Yining
• Is Part Of: Journal of physics. Volume 1633(2020)
• Publication Date: 2020-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1633/1/012166
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The Aluminum Conductor Composite Core (ACCC) has been considered one of the solutions for massively increasing requirements for the electricity power transmission in China due to its superiority in weight, strength and ampacity. Yet the popularize of ACCC lines suffer from damages caused during the construction, which may result in line broke in the future. In this paper, an automatic defect detection method based on Deep Convolution Network is proposed. Image classification framework with Inception-Resnet structure as backbone is applied. With the online self-designed robot, the proposed method can effectively detect the defects such as fracture, splitting and distortion, with a recall rate over 90%.