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ESF-DETR: a real-time and high-precision detection model for cigarette appearance.

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Titel: ESF-DETR: a real-time and high-precision detection model for cigarette appearance.
Autoren: Ding, Yingchao, Yuan, Guowu, Zhou, Hao, Wu, Hao
Quelle: Journal of Real-Time Image Processing; Apr2025, Vol. 22 Issue 2, p1-13, 13p
Abstract: The quality of cigarettes is greatly affected by appearance defects. Achieving automatic detection of these defects with high precision and speed has been a critical concern for cigarette factories. To meet the needs of manufacturers in detecting appearance defects in cigarettes, this paper proposes a model based on DETR (DEtection TRansformer) for detecting cigarette appearance defects. The model integrates EfficientViT, SENetV2, and FuNet (Full-scale Feature Fusion Network), called ESF-DETR. First, EfficientViT serves as the backbone feature extraction network, substantially reducing model parameters and enhancing feature extraction efficiency. Second, SENetV2 is introduced at the end of the backbone network to improve feature expression accuracy and global information integration capability. Third, the Full-scale Feature Fusion Network (FuNet) is proposed as the encoder, further reducing model parameters while increasing spatial location and high-level semantic information across each feature layer. The proposed ESF-DETR model achieves a mAP of 96.0% with a parameter count of 10.1M. Compared to the original model, the mAP has increased by 4.4%, while the number of parameters has decreased by 49.8%. Additionally, the detection speed reaches 500 FPS, satisfying cigarette production lines' accuracy and speed requirements. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Real-Time Image Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: ESF-DETR: a real-time and high-precision detection model for cigarette appearance.
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Yingchao%22">Ding, Yingchao</searchLink><br /><searchLink fieldCode="AR" term="%22Yuan%2C+Guowu%22">Yuan, Guowu</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Hao%22">Zhou, Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Hao%22">Wu, Hao</searchLink>
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  Data: Journal of Real-Time Image Processing; Apr2025, Vol. 22 Issue 2, p1-13, 13p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The quality of cigarettes is greatly affected by appearance defects. Achieving automatic detection of these defects with high precision and speed has been a critical concern for cigarette factories. To meet the needs of manufacturers in detecting appearance defects in cigarettes, this paper proposes a model based on DETR (DEtection TRansformer) for detecting cigarette appearance defects. The model integrates EfficientViT, SENetV2, and FuNet (Full-scale Feature Fusion Network), called ESF-DETR. First, EfficientViT serves as the backbone feature extraction network, substantially reducing model parameters and enhancing feature extraction efficiency. Second, SENetV2 is introduced at the end of the backbone network to improve feature expression accuracy and global information integration capability. Third, the Full-scale Feature Fusion Network (FuNet) is proposed as the encoder, further reducing model parameters while increasing spatial location and high-level semantic information across each feature layer. The proposed ESF-DETR model achieves a mAP of 96.0% with a parameter count of 10.1M. Compared to the original model, the mAP has increased by 4.4%, while the number of parameters has decreased by 49.8%. Additionally, the detection speed reaches 500 FPS, satisfying cigarette production lines' accuracy and speed requirements. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Real-Time Image Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11554-025-01632-y
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              Text: Apr2025
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