Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2023
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| Online toegang: | http://hdl.handle.net/10361/19356 |
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10361-193562023-08-08T21:02:07Z Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch Uddin, Md. Minhaz Foysal, Sadi Mahmud Rahman, Sadia Risti, Nushara Tazrin Sarmin, Sanzeda Akter Rhaman, Dr. Md. Khalilur Department of Computer Science and Engineering, Brac University TILDA YOLOv7 YOLOv7x YOLOv7-W6 CNN RCNN DCNN Faster-RCNN Neural Networks Real-Time Computer vision Fault Defect Detection Garments Hole Stitch Seam Roboflow Signal detection. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-44). In the era of computer vision to overcome challenges, the introduction of the YOLO model revolutionized real-time computer vision approaches. In the garment industry, the inception of products plays a significant role while increasing the processing time with a good accuracy rate is the big challenge here. A real-time garments defect detection approach using YOLOv7, YOLOv7x, and YOLOv7-w6 on a primary dataset is proposed with a good FPS rate and better accuracy. Maximum traditional garments inception approaches focused on end product defects while this model suggests detecting defects on the sewing phase so that the cost of the rejected end product can be optimized by detecting them before a product goes through all the phases. For this our research is more focused on three subclasses of Seam, Stitch, and Hole related to sewing phase defects. To increase the detection rate, the hyperparameter tuning technique is applied to the YOLOv7 model. Three models are proposed based on pre-trained weights of YOLOv7, YOLOv7x, and YOLOv7- w6 to compare the accuracy and FPS rate in terms of implementation in real-world projects. Md. Minhaz Uddin Sadi Mahmud Foysal Sadia Rahman Nushara Tazrin Risti Sanzeda Akter Sarmin B. Computer Science 2023-08-08T05:39:07Z 2023-08-08T05:39:07Z 2023 2023-01 Thesis ID: 19101013 ID: 22241046 ID: 19141001 ID: 22241041 ID: 19101026 http://hdl.handle.net/10361/19356 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 44 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
TILDA YOLOv7 YOLOv7x YOLOv7-W6 CNN RCNN DCNN Faster-RCNN Neural Networks Real-Time Computer vision Fault Defect Detection Garments Hole Stitch Seam Roboflow Signal detection. |
| spellingShingle |
TILDA YOLOv7 YOLOv7x YOLOv7-W6 CNN RCNN DCNN Faster-RCNN Neural Networks Real-Time Computer vision Fault Defect Detection Garments Hole Stitch Seam Roboflow Signal detection. Uddin, Md. Minhaz Foysal, Sadi Mahmud Rahman, Sadia Risti, Nushara Tazrin Sarmin, Sanzeda Akter Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Rhaman, Dr. Md. Khalilur |
| author_facet |
Rhaman, Dr. Md. Khalilur Uddin, Md. Minhaz Foysal, Sadi Mahmud Rahman, Sadia Risti, Nushara Tazrin Sarmin, Sanzeda Akter |
| format |
Thesis |
| author |
Uddin, Md. Minhaz Foysal, Sadi Mahmud Rahman, Sadia Risti, Nushara Tazrin Sarmin, Sanzeda Akter |
| author_sort |
Uddin, Md. Minhaz |
| title |
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch |
| title_short |
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch |
| title_full |
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch |
| title_fullStr |
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch |
| title_full_unstemmed |
Real-time garments defects detection at the sewing phase to optimize waste cost using YOLOv7, YOLOv7x, YOLOv7-w6 and Pytorch |
| title_sort |
real-time garments defects detection at the sewing phase to optimize waste cost using yolov7, yolov7x, yolov7-w6 and pytorch |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19356 |
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| _version_ |
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