Defect detection on aluminum surfaces based on deep learning

doi:10.3969/j.issn.1000-5641.201921021

Abstract

Abstract: With the application of information technology to industrial manufacturing, big data research in industrial manufacturing has become an important basis for realizing intelligent manufacturing and helping the government to guide the transformation and upgrade of manufacturing enterprises. In traditional metal manufacturing industries, like steel and aluminum, there are challenges, such as uncalibrated and rudimentary production techniques. Therefore, it is urgent to improve production processes and production efficiency by using newer generation information technology, such as artificial intelligence. The surface of aluminum, for example, must be inspected during the manufacturing process. Existing surface defect detection techniques for aluminum materials is limited to traditional manual naked eye inspection-which is very laborious-or traditional machine vision algorithms-which have a low rate of detection. Hence, surface defects cannot be accurately judged in a timely fashion. This paper studies the use of deep learning for surface defect detection of aluminum materials. Firstly, two major target detection algorithms, Faster R-CNN and YOLOv3, are used to detect the defects on a set of aluminum materials. Then, based on the YOLOv3 algorithm, the detection ability of small defects on aluminum surfaces was improved. We conducted experiments on the “Aluminum Profile Defect Recognition” dataset provided by the Guangdong Industrial Intelligent Manufacturing Big Data Innovation Competition. The experimental results showed that the mean average precision (mAP) of the improved algorithm is 3.4% higher than that of YOLOv 3 and 1.8% higher than that of Faster R-CNN.

Key words: aluminum defect, detection, machine vision, deep learning

CLC Number:

TP391

ZHANG Xu, HUANG Dingjiang. Defect detection on aluminum surfaces based on deep learning[J]. Journal of East China Normal University(Natural Science), 2020, 2020(6): 105-114.

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