Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet
Defective stitch inspection is an essential part of garment manufacturing quality assurance. Traditional mechanical defect detection systems are effective, but they are usually customized with handcrafted features that must be operated by a human. Deep learning approaches have recently demonstrated...
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| Format: | Article |
| Language: | English |
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Asociación Española para la Inteligencia Artificial
2022-11-01
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| Series: | Inteligencia Artificial |
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| Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/862 |
| _version_ | 1811303891592019968 |
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| author | Quoc Toan Nguyen |
| author_facet | Quoc Toan Nguyen |
| author_sort | Quoc Toan Nguyen |
| collection | DOAJ |
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Defective stitch inspection is an essential part of garment manufacturing quality assurance. Traditional mechanical defect detection systems are effective, but they are usually customized with handcrafted features that must be operated by a human. Deep learning approaches have recently demonstrated exceptional performance in a wide range of computer vision applications. The requirement for precise detail evaluation, combined with the small size of the patterns, undoubtedly increases the difficulty of identification. Therefore, image segmentation (semantic segmentation) was employed for this task. It is identified as a vital research topic in the field of computer vision, being indispensable in a wide range of real-world applications. Semantic segmentation is a method of labeling each pixel in an image. This is in direct contrast to classification, which assigns a single label to the entire image. And multiple objects of the same class are defined as a single entity. DeepLabV3+ architecture, with encoder-decoder architecture, is the proposed technique. EfficientNet models (B0-B2) were applied as encoders for experimental processes. The encoder is utilized to encode feature maps from the input image. The encoder's significant information is used by the decoder for upsampling and reconstruction of output. Finally, the best model is DeeplabV3+ with EfficientNetB1 which can classify segmented defective sewing stitches with superior performance (MeanIoU: 94.14%).
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| first_indexed | 2024-04-13T07:57:23Z |
| format | Article |
| id | doaj.art-ddb5ca6898fc4867b93517a29326c201 |
| institution | Directory Open Access Journal |
| issn | 1137-3601 1988-3064 |
| language | English |
| last_indexed | 2024-04-13T07:57:23Z |
| publishDate | 2022-11-01 |
| publisher | Asociación Española para la Inteligencia Artificial |
| record_format | Article |
| series | Inteligencia Artificial |
| spelling | doaj.art-ddb5ca6898fc4867b93517a29326c2012022-12-22T02:55:23ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642022-11-01257010.4114/intartif.vol25iss70pp64-76Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNetQuoc Toan Nguyen0Department of Electronic and Electrical Engineering, Hongik University, South Korea Defective stitch inspection is an essential part of garment manufacturing quality assurance. Traditional mechanical defect detection systems are effective, but they are usually customized with handcrafted features that must be operated by a human. Deep learning approaches have recently demonstrated exceptional performance in a wide range of computer vision applications. The requirement for precise detail evaluation, combined with the small size of the patterns, undoubtedly increases the difficulty of identification. Therefore, image segmentation (semantic segmentation) was employed for this task. It is identified as a vital research topic in the field of computer vision, being indispensable in a wide range of real-world applications. Semantic segmentation is a method of labeling each pixel in an image. This is in direct contrast to classification, which assigns a single label to the entire image. And multiple objects of the same class are defined as a single entity. DeepLabV3+ architecture, with encoder-decoder architecture, is the proposed technique. EfficientNet models (B0-B2) were applied as encoders for experimental processes. The encoder is utilized to encode feature maps from the input image. The encoder's significant information is used by the decoder for upsampling and reconstruction of output. Finally, the best model is DeeplabV3+ with EfficientNetB1 which can classify segmented defective sewing stitches with superior performance (MeanIoU: 94.14%). https://journal.iberamia.org/index.php/intartif/article/view/862Computer VisionSemantic SegmentationConvolutional Neural NetworksDeeplabV3 EfficientNet |
| spellingShingle | Quoc Toan Nguyen Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet Inteligencia Artificial Computer Vision Semantic Segmentation Convolutional Neural Networks DeeplabV3 EfficientNet |
| title | Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet |
| title_full | Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet |
| title_fullStr | Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet |
| title_full_unstemmed | Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet |
| title_short | Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet |
| title_sort | defective sewing stitch semantic segmentation using deeplabv3 and efficientnet |
| topic | Computer Vision Semantic Segmentation Convolutional Neural Networks DeeplabV3 EfficientNet |
| url | https://journal.iberamia.org/index.php/intartif/article/view/862 |
| work_keys_str_mv | AT quoctoannguyen defectivesewingstitchsemanticsegmentationusingdeeplabv3andefficientnet |