An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke
Deep Neural Networks are designed explicitly for visionbased applications. They have become a standard method for image classification, object detection, real-time processing capabilities, and other computer vision tasks. With the intervention of human beings, it is challenging for businesses to ver...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01007.pdf |
| _version_ | 1797807673172819968 |
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| author | Kulkarni Uday Naregal Kiran Farande Vishal Guttigoli Soumya Angadi Apoorva Ujwane Raunak |
| author_facet | Kulkarni Uday Naregal Kiran Farande Vishal Guttigoli Soumya Angadi Apoorva Ujwane Raunak |
| author_sort | Kulkarni Uday |
| collection | DOAJ |
| description | Deep Neural Networks are designed explicitly for visionbased applications. They have become a standard method for image classification, object detection, real-time processing capabilities, and other computer vision tasks. With the intervention of human beings, it is challenging for businesses to verify that the product is appropriately created without any glitches, as this could result in human errors due to a lack of training, which could further cause losses and several complications. Thus, there is a need for a machine learning-based solution that improves work efficiency and accuracy in identifying processed and unprocessed parts. Machine Learning models have several advantages over human intervention in tasks, including consistency, speed, scalability, costeffectiveness, and improvement over time. These advantages can help organizations improve their operations and achieve better outcomes. To do this, we propose an approach that involves building a model using an object detection method, You Only Look Once (YOLO), which is then deployed on a Raspberry Pi and integrated with the assembly line to carry out the task of validating the processed parts by achieving 98% accuracy. |
| first_indexed | 2024-03-13T06:26:10Z |
| format | Article |
| id | doaj.art-c05c7ea7d4924770bb52b31f36b5dd87 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-13T06:26:10Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-c05c7ea7d4924770bb52b31f36b5dd872023-06-09T09:24:03ZengEDP SciencesITM Web of Conferences2271-20972023-01-01530100710.1051/itmconf/20235301007itmconf_icdsia2023_01007An Object Detection Approach for Automated Detection of Groove Line in Tube YokeKulkarni Uday0Naregal Kiran1Farande Vishal2Guttigoli Soumya3Angadi Apoorva4Ujwane Raunak5KLE Technological UniversityKLE Technological UniversityKLE Technological UniversityKLE Technological UniversityKLE Technological UniversityDana Anand India Private LimitedDeep Neural Networks are designed explicitly for visionbased applications. They have become a standard method for image classification, object detection, real-time processing capabilities, and other computer vision tasks. With the intervention of human beings, it is challenging for businesses to verify that the product is appropriately created without any glitches, as this could result in human errors due to a lack of training, which could further cause losses and several complications. Thus, there is a need for a machine learning-based solution that improves work efficiency and accuracy in identifying processed and unprocessed parts. Machine Learning models have several advantages over human intervention in tasks, including consistency, speed, scalability, costeffectiveness, and improvement over time. These advantages can help organizations improve their operations and achieve better outcomes. To do this, we propose an approach that involves building a model using an object detection method, You Only Look Once (YOLO), which is then deployed on a Raspberry Pi and integrated with the assembly line to carry out the task of validating the processed parts by achieving 98% accuracy.https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01007.pdf |
| spellingShingle | Kulkarni Uday Naregal Kiran Farande Vishal Guttigoli Soumya Angadi Apoorva Ujwane Raunak An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke ITM Web of Conferences |
| title | An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke |
| title_full | An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke |
| title_fullStr | An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke |
| title_full_unstemmed | An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke |
| title_short | An Object Detection Approach for Automated Detection of Groove Line in Tube Yoke |
| title_sort | object detection approach for automated detection of groove line in tube yoke |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01007.pdf |
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