Defective sealing of outer packaging can result in squeezed, deformed, or fallen-off products, inevitably leading to losses for businesses or individuals. Thus, detecting tape-sealing defects in external packaging is crucial in industrial manufacturing. Systems for detecting surface defects based on visual perception are popular in industrial quality inspection. Nevertheless, traditional surface defect detection methods demonstrate limited precision and slow performance. Multi-scale, multi-type defects, numerous small targets, and complex background interference characterize surface defects in industrial products. Detecting minor, multi-scale defects amidst complex background interference is significantly challenging. Therefore, developing algorithms that can accurately detect industrial defects remains a challenging problem. Consequently, we propose a model that uses YOLOv8 to detect tape-sealing defects of cigarette carton. Using diverse branch blocks (DBB), The backbone extracts multi-scale representations and enlarges the effective receptive field. Secondly, implementing a combined module (CCFM+C2fiRMA) in the Neck enhances the detection accuracy. Also, we adopt focal loss to further enhance the detection of tiny objects. Finally, experimental results show that our model achieves 98.1% mAP 50 and 67.8% mAP 50−95 on the QZU-DET dataset, outperforming YOLOv8n by 3.2 and 2.2% points, respectively. In addition, our method attains an F1 score of 91.6%, indicating strong detection reliability. Overall, the proposed model enables efficient and accurate tape-sealing defect detection in practical industrial scenarios.
Hou et al. (2026) studied this question.