@inproceedings{d7d7659352c24adeb427b6007a98c72f,
title = "Improving Classification Accuracy in Paint Surface Inspection Using Image Processing and Deep Learning",
abstract = "In recent years, research has been conducted on defect detection and classification using image processing and deep learning as alternatives to visual inspection. This paper proposes a method to detect defects by applying embossing and histogram equalization to images of painted surfaces and classify defects using ResNet50. The proposed method is capable of simultaneously classifying multiple defects in an image. Furthermore, we show that the proposed method can improve the accuracy of defect extraction compared to the conventional method.",
keywords = "deep learning, defect classification, defect detection, embossing",
author = "Motoki Sawada and Yuta Fuji and Naoyuki Aikawa",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024 ; Conference date: 11-08-2024 Through 14-08-2024",
year = "2024",
doi = "10.1109/MWSCAS60917.2024.10658801",
language = "English",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "707--711",
booktitle = "2024 IEEE 67th International Midwest Symposium on Circuits and Systems, MWSCAS 2024",
}