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- Title
Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning.
- Authors
Wei, Ruofeng; Bi, Yunbo
- Abstract
Aluminum profile surface defects can greatly affect the performance, safety, and reliability of products. Traditional human-based visual inspection has low accuracy and is time consuming, and machine vision-based methods depend on hand-crafted features that need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect-detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, and 43.3% average precision (AP) for the 10 defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.
- Subjects
ALUMINUM; SURFACE defects; COMPUTER vision; ROBUST control; DEEP learning
- Publication
Materials (1996-1944), 2019, Vol 12, Issue 10, p1681
- ISSN
1996-1944
- Publication type
Article
- DOI
10.3390/ma12101681