Design of image classification system for fabric inspection process using Raspberry Pi
This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by...
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| Format: | Article |
| Language: | English |
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Indonesian Institute of Sciences
2024-07-01
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| Series: | Journal of Mechatronics, Electrical Power, and Vehicular Technology |
| Subjects: | |
| Online Access: | https://mev.brin.go.id/mev/article/view/863 |
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| author | Emmanuel Agung Nugroho Joga Dharma Setiawan Munadi Munadi Diki Diki |
| author_facet | Emmanuel Agung Nugroho Joga Dharma Setiawan Munadi Munadi Diki Diki |
| author_sort | Emmanuel Agung Nugroho |
| collection | DOAJ |
| description | This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %. |
| format | Article |
| id | doaj-art-e0bcb1e6bfcb41928bb0f024b80325fb |
| institution | OA Journals |
| issn | 2087-3379 2088-6985 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Indonesian Institute of Sciences |
| record_format | Article |
| series | Journal of Mechatronics, Electrical Power, and Vehicular Technology |
| spelling | doaj-art-e0bcb1e6bfcb41928bb0f024b80325fb2025-08-20T02:17:04ZengIndonesian Institute of SciencesJournal of Mechatronics, Electrical Power, and Vehicular Technology2087-33792088-69852024-07-01151576710.55981/j.mev.2024.863342Design of image classification system for fabric inspection process using Raspberry PiEmmanuel Agung Nugroho0Joga Dharma Setiawan1Munadi Munadi2Diki Diki3-Indorama Engineering Polytechnic, Purwakarta, 41152, Indonesia -Faculty of Engineering, Diponegoro University Semarang, IndonesiaFaculty of Engineering, Diponegoro University, Semarang, IndonesiaFaculty of Engineering, Diponegoro University Semarang, IndonesiaIndorama Engineering Polytechnic Purwakarta, 41152, IndonesiaThis research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.https://mev.brin.go.id/mev/article/view/863machine learningimage processingraspberry piinference timeimage classification |
| spellingShingle | Emmanuel Agung Nugroho Joga Dharma Setiawan Munadi Munadi Diki Diki Design of image classification system for fabric inspection process using Raspberry Pi Journal of Mechatronics, Electrical Power, and Vehicular Technology machine learning image processing raspberry pi inference time image classification |
| title | Design of image classification system for fabric inspection process using Raspberry Pi |
| title_full | Design of image classification system for fabric inspection process using Raspberry Pi |
| title_fullStr | Design of image classification system for fabric inspection process using Raspberry Pi |
| title_full_unstemmed | Design of image classification system for fabric inspection process using Raspberry Pi |
| title_short | Design of image classification system for fabric inspection process using Raspberry Pi |
| title_sort | design of image classification system for fabric inspection process using raspberry pi |
| topic | machine learning image processing raspberry pi inference time image classification |
| url | https://mev.brin.go.id/mev/article/view/863 |
| work_keys_str_mv | AT emmanuelagungnugroho designofimageclassificationsystemforfabricinspectionprocessusingraspberrypi AT jogadharmasetiawan designofimageclassificationsystemforfabricinspectionprocessusingraspberrypi AT munadimunadi designofimageclassificationsystemforfabricinspectionprocessusingraspberrypi AT dikidiki designofimageclassificationsystemforfabricinspectionprocessusingraspberrypi |