Preprint Article Version 1 This version is not peer-reviewed

Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

Version 1 : Received: 28 April 2019 / Approved: 29 April 2019 / Online: 29 April 2019 (09:37:07 CEST)

A peer-reviewed article of this Preprint also exists.

Wei, R.; Bi, Y. Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning. Materials 2019, 12, 1681. Wei, R.; Bi, Y. Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning. Materials 2019, 12, 1681.

Journal reference: Materials 2019, 12, 1681
DOI: 10.3390/ma12101681

Abstract

Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which 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%, 43.3% average precision(AP) for the ten 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.

Subject Areas

aluminum profile surface defects; multiscale defect detection network; deep learning; average precision(AP); saliency maps

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