Machine Vision and Deep Learning based Rubber Gasket Defect Detection
| العنوان: | Machine Vision and Deep Learning based Rubber Gasket Defect Detection |
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| المؤلفون: | Matthew J. Bolger, Huan-Ning Pan, Chao-Ching Ho, Eugene Su, Po-Chieh Li |
| المصدر: | Advances in Technology Innovation, Vol 5, Iss 2 (2020) |
| بيانات النشر: | Taiwan Association of Engineering and Technology Innovation, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Environmental Engineering, convolutional neural networks (CNN), General Computer Science, Renewable Energy, Sustainability and the Environment, business.industry, Machine vision, Computer science, deep residual learning, Gasket, Deep learning, General Engineering, Energy Engineering and Power Technology, deep learning, traditional rule-based strategy, image processing, image recognition, Natural rubber, Management of Technology and Innovation, visual_art, lcsh:Technology (General), visual_art.visual_art_medium, lcsh:T1-995, Computer vision, Artificial intelligence, business |
| الوصف: | This study develops an automated optical inspection system for silicone rubber gaskets using traditional rule-based and deep learning detection techniques. The specific object of interest is a 5 mm × 10 mm × 5 mm mobile device power supply connector gasket that provides protection against foreign body inclusion and water ingression. The proposed system can detect a total of five characteristic defects introduced during the mold-based manufacture process, which range from 10-100 μm. The deep learning detection strategies in this system employ convolutional neural networks (CNN) developed using the TensorFlow open-source library. Through both high dynamic range image capture and image generation techniques, accuracies of 100% and 97% are achieved for notch and residual glue defect predictions, respectively. |
| اللغة: | English |
| تدمد: | 2518-2994 2415-0436 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d59631063798ea987d136b688e302dc8 http://ojs.imeti.org/index.php/AITI/article/view/4278 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....d59631063798ea987d136b688e302dc8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 25182994 24150436 |
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