ASC-YOLOv8n: Enhanced multi-scale feature fusion for accurate detection of four cigar appearance defects
- 1Zhengzhou Tobacco Research Institute of CNTC
- 2China Tobacco Sichuan Industrial Co., Ltd.
- 3China Tobacco Shandong Industrial Co., Ltd.
Abstract
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 ([email protected]) 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.
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BibTeX
@article{yang2026ascyolov8n,
title = {{ASC-YOLOv8n: Enhanced multi-scale feature fusion for accurate detection of four cigar appearance defects}},
author = {Xinan Yang and Tao Liu and Xinyi Li and Xi Hu and Jing Gao and Xiaolong Yi and Peng Guo and Rui Chen and Wu Wen and Rongya Zhang and Wenkui Zhu},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62533-z},
url = {https://doi.org/10.1038/s41598-026-62533-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - ASC-YOLOv8n: Enhanced multi-scale feature fusion for accurate detection of four cigar appearance defects
AU - Xinan Yang
AU - Tao Liu
AU - Xinyi Li
AU - Xi Hu
AU - Jing Gao
AU - Xiaolong Yi
AU - Peng Guo
AU - Rui Chen
AU - Wu Wen
AU - Rongya Zhang
AU - Wenkui Zhu
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62533-z
UR - https://doi.org/10.1038/s41598-026-62533-z
AB - 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 ([email protected]) 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.
SN - 2045-2322
ER -