Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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.

Keywords

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

Subject

ENGINEERING, Mechanical Engineering

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