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<p>The appearance defects of cigars can significantly compromise their overall quality, with detection currently relying mainly on manual inspection, a process that is time-consuming and inefficient. The ASC-YOLOv8n model has been proposed for high-precision automated defect detection in full-leaf handmade cigars, targeting defects such as green spots, holes, breaks, and tail breaks during production. This model incorporates several key improvements: it integrates an ASC (Adaptive Spatial Context) module to provide a more hierarchical receptive field, enhancing its ability to detect intricate defect patterns; replaces the conventional C2f module with a more advanced Fusion module, which combines multiple feature maps to improve feature extraction capabilities; and employs a novel WIoU (Weighted Intersection over Union) localization loss function, significantly refining defect localization precision. Experimental results show that the ASC-YOLOv8n model achieves a performance boost, with the mean average precision at an IoU threshold of 0.5 (mAP@0.5) increasing by 1.7% points to 94.00%, outperforming the baseline YOLOv8n model. This demonstrates the model’s effectiveness in accurately identifying critical cigar defects, making it a reliable and robust solution for intelligent cigar inspection, and contributing to enhanced quality control in cigar production.</p>

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ASC-YOLOv8n: Enhanced multi-scale feature fusion for accurate detection of four cigar appearance defects

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

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  • Xinan Yang,
  • Tao Liu,
  • Xinyi Li,
  • Xi Hu,
  • Jing Gao,
  • Xiaolong Yi,
  • Peng Guo,
  • Rui Chen,
  • Wu Wen,
  • Rongya Zhang,
  • Wenkui Zhu

摘要

The appearance defects of cigars can significantly compromise their overall quality, with detection currently relying mainly on manual inspection, a process that is time-consuming and inefficient. The ASC-YOLOv8n model has been proposed for high-precision automated defect detection in full-leaf handmade cigars, targeting defects such as green spots, holes, breaks, and tail breaks during production. This model incorporates several key improvements: it integrates an ASC (Adaptive Spatial Context) module to provide a more hierarchical receptive field, enhancing its ability to detect intricate defect patterns; replaces the conventional C2f module with a more advanced Fusion module, which combines multiple feature maps to improve feature extraction capabilities; and employs a novel WIoU (Weighted Intersection over Union) localization loss function, significantly refining defect localization precision. Experimental results show that the ASC-YOLOv8n model achieves a performance boost, with the mean average precision at an IoU threshold of 0.5 (mAP@0.5) increasing by 1.7% points to 94.00%, outperforming the baseline YOLOv8n model. This demonstrates the model’s effectiveness in accurately identifying critical cigar defects, making it a reliable and robust solution for intelligent cigar inspection, and contributing to enhanced quality control in cigar production.