Huang Liang, Shen Qiongxia, Jiang Chao, Yang You
School of Electronic Information and Communications, Huazhong University of Science & Technology, Wuhan 430074, China.
Fiberhome Telecommunication Technologies Co., Ltd., Wuhan 430205, China.
Sensors (Basel). 2024 Oct 21;24(20):6752. doi: 10.3390/s24206752.
In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.
在卷烟制造行业，机器视觉和人工智能算法已被用于通过检测产品缺陷来提高生产效率。然而，对于具有复杂图案的卷烟实现高精度和实时缺陷检测仍然是一个挑战。为了解决这些问题，本研究提出了一种基于RESNET18的模型，并结合特征增强算法，以提高检测精度。此外，还设计了一种方法，将该模型部署在具有高并行处理能力的现场可编程门阵列（FPGA）上，以实现高速检测。实验结果表明，所提出的检测模型在卷烟滤嘴缺陷数据集上实现了95.88%的检测精度，端到端检测速度仅为9.38毫秒。
