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Convolutional Neural Network-Based Metal Surface Defect Detection
Ida Bagus Krishna Yoga Utama,Yeong Min Jang 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
The visual inspection using computer vision technology is growing rapidly nowadays. The number of industries that relies on automatic defect detection is rising due to some limitations when doing defect detection manually by a worker. The automatic defect detection also benefits the industry because it will help increase the quality control of production line and in the end it helps to maintain the product quality. A convolutional neural network is developed in order to classify six types of defect on metal surface. The result is promising, the developed model able to recognize 95.8% of testing data correctly.