An efficient neural architecture search based deep learning algorithm for surface defects detection of T-beam
by Jianbo Li; Guirong Hou; Hong Xiang; Zhiwen Lei; Shaomiao Chen
International Journal of Embedded Systems (IJES), Vol. 16, No. 2, 2023

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%.

Online publication date: Wed, 31-Jan-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com