Title: An efficient neural architecture search based deep learning algorithm for surface defects detection of T-beam

Authors: Jianbo Li; Guirong Hou; Hong Xiang; Zhiwen Lei; Shaomiao Chen

Addresses: Hunan Provincial Transportation Construction Engineering Supervision Co., Ltd., Hunan Communications Research Institute Co. Ltd., Changsha, China ' Hunan Provincial Transportation Construction Engineering Supervision Co., Ltd., Hunan Communications Research Institute Co. Ltd., Changsha, China ' Hunan Provincial Transportation Construction Engineering Supervision Co., Ltd., Hunan Communications Research Institute Co. Ltd., Changsha, China ' Hunan Key Laboratory of Service Computing and New Software Service Technology, School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China ' Hunan Key Laboratory of Service Computing and New Software Service Technology, School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

Abstract: The surface defect detection of T-beam of highway bridge in the traditional quality control process is mainly based on naked eye observation. The detection results are too dependent on personal experience and require a significant amount of work. This paper applies the convolutional neural network constructed by differentiable neural architecture search method to the automatic detection of surface defect on T-beams. And a reduction cell of the modified network is designed to search for the normal cell to improve the search efficiency. The results show that the search efficiency of the network is improved and the search time is reduced by about 31%.

Keywords: T-beams quality; surface defect detection; convolutional neural network; CNN; neural architecture search; NAS.

DOI: 10.1504/IJES.2023.136391

International Journal of Embedded Systems, 2023 Vol.16 No.2, pp.153 - 160

Received: 12 May 2023
Accepted: 24 Aug 2023
Published online: 31 Jan 2024 *

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