关键词: 香烟外观缺陷检测, YOLOv8n, 小目标检测, LAMP, 模型部署

Abstract: To address challenges of low precision and recall in small-target defect detection for cigarette inspection, this study proposes CSDD-YOLO(Cigarette Stick Defect Detection YOLO), an enhanced YOLOv8n (You Only Look Once version 8n)-based algorithm. Framework integrates a dedicated P2 detection head with a MscalSeq (Multi-scale Sequence ) module to strengthen multi-scale feature fusion for tiny defects. A lightweight C3k2 backbone replaces C2f modules to balance efficiency and deep feature extraction, while a CKAttention (Choose Kernel Attention ) mechanism enhances discriminative capabilities. Further optimized via LAMP (Layer Adaptive Magnitude-based Pruning), model reduces parameters significantly without compromising performance. Experiments on real-world datasets demonstrate a mean Average Precision(mAP) of 94.7% (7.5% higher than baseline) with only 36% of original model size. Deployed via C++/ONNX Runtime(Open Neural Network Exchange Runtime), solution achieves real-time detection accuracy, proving highly applicable for industrial quality control systems.

Key words: cigarette appearance defect detection, YOLOv8n, small target detection, Layer Adaptive Magnitude-based Pruning &#40, LAMP&#41