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

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.