AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs
Abstract Defect detection in cigarette packaging is a crucial process for ensuring product quality meets industry standards within the tobacco manufacturing sector. Defect detection methods based on deep learning have significantly enhanced efficiency. However, challenges remain in addressing issues...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.13272 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846121445127618560 |
|---|---|
| author | Peng Dong Yongsheng Wang Qianyuan Yu Weihua Feng Guohao Zong |
| author_facet | Peng Dong Yongsheng Wang Qianyuan Yu Weihua Feng Guohao Zong |
| author_sort | Peng Dong |
| collection | DOAJ |
| description | Abstract Defect detection in cigarette packaging is a crucial process for ensuring product quality meets industry standards within the tobacco manufacturing sector. Defect detection methods based on deep learning have significantly enhanced efficiency. However, challenges remain in addressing issues such as blurred boundary textures of cigarette pack defects and the complex differentiability and similarity among defects. Consequently, this study presents the design of a defect detector for cigarette packs that is sensitive to detail features, named AMC‐YOLO. Initially, an Adaptive Spatial Weight Perception (ASWP) module is designed to emphasize local information from different regions during the downsampling process and integrate effective features. Additionally, a Multidimensional Aggregation Radiative Feature Pyramid Networks (MARFPN) is proposed to aggregate multi‐scale semantic information across dimensions and relay it back to various levels within the network to facilitate the learning of more refined feature. Lastly, a Cross‐Layer Collaborative Detection Head (CLCDH) is introduced to further weight and fuse the contextual information between local and global aspects. Experimental results demonstrate that AMC‐YOLO outperforms state‐of‐the‐art methods on the Cigarette Pack Defect and GC10‐DET datasets, exhibiting the highest detection precision and excellent generalization. These findings highlight the significant potential for the application of AMC‐YOLO in cigarette pack defect detection. |
| format | Article |
| id | doaj-art-758c0a1caf5c43b9b9be56a1639da479 |
| institution | Kabale University |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-758c0a1caf5c43b9b9be56a1639da4792024-12-16T04:00:31ZengWileyIET Image Processing1751-96591751-96672024-12-0118144873488610.1049/ipr2.13272AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packsPeng Dong0Yongsheng Wang1Qianyuan Yu2Weihua Feng3Guohao Zong4Henan Institute of Advanced Technology Zhengzhou University Zhengzhou ChinaZhengzhou Tobacco Research of CNTC China National Tobacco Corporation Zhengzhou ChinaZhengzhou Tobacco Research of CNTC China National Tobacco Corporation Zhengzhou ChinaZhengzhou Tobacco Research of CNTC China National Tobacco Corporation Zhengzhou ChinaZhengzhou Tobacco Research of CNTC China National Tobacco Corporation Zhengzhou ChinaAbstract Defect detection in cigarette packaging is a crucial process for ensuring product quality meets industry standards within the tobacco manufacturing sector. Defect detection methods based on deep learning have significantly enhanced efficiency. However, challenges remain in addressing issues such as blurred boundary textures of cigarette pack defects and the complex differentiability and similarity among defects. Consequently, this study presents the design of a defect detector for cigarette packs that is sensitive to detail features, named AMC‐YOLO. Initially, an Adaptive Spatial Weight Perception (ASWP) module is designed to emphasize local information from different regions during the downsampling process and integrate effective features. Additionally, a Multidimensional Aggregation Radiative Feature Pyramid Networks (MARFPN) is proposed to aggregate multi‐scale semantic information across dimensions and relay it back to various levels within the network to facilitate the learning of more refined feature. Lastly, a Cross‐Layer Collaborative Detection Head (CLCDH) is introduced to further weight and fuse the contextual information between local and global aspects. Experimental results demonstrate that AMC‐YOLO outperforms state‐of‐the‐art methods on the Cigarette Pack Defect and GC10‐DET datasets, exhibiting the highest detection precision and excellent generalization. These findings highlight the significant potential for the application of AMC‐YOLO in cigarette pack defect detection.https://doi.org/10.1049/ipr2.13272computer visionobject detectionproduct quality |
| spellingShingle | Peng Dong Yongsheng Wang Qianyuan Yu Weihua Feng Guohao Zong AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs IET Image Processing computer vision object detection product quality |
| title | AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs |
| title_full | AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs |
| title_fullStr | AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs |
| title_full_unstemmed | AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs |
| title_short | AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs |
| title_sort | amc yolo improved yolov8 based defect detection for cigarette packs |
| topic | computer vision object detection product quality |
| url | https://doi.org/10.1049/ipr2.13272 |
| work_keys_str_mv | AT pengdong amcyoloimprovedyolov8baseddefectdetectionforcigarettepacks AT yongshengwang amcyoloimprovedyolov8baseddefectdetectionforcigarettepacks AT qianyuanyu amcyoloimprovedyolov8baseddefectdetectionforcigarettepacks AT weihuafeng amcyoloimprovedyolov8baseddefectdetectionforcigarettepacks AT guohaozong amcyoloimprovedyolov8baseddefectdetectionforcigarettepacks |