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 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.