改进YOLOv8的烟盒缺陷检测算法

Improved YOLOv8 cigarette box defect detection algorithm

近年来,人们对于烟盒包装质量的要求越来越高,在现代化的生产中,烟盒的生产速度大幅度提升,生产设备也都实现了智能化。但是,对于烟盒的表面质量检测仍然采用人工的方式进行。针对人工烟盒表面缺陷检测容易发生漏检、错检等问题,提出了一种基于改进YOLOv8的烟盒缺陷检测算法。首先在YOLOv8的颈部网络引入了GD机制,提高了模型对于不同层级间的信息融合能力;其次加入了尺度序列特征融合模块,增强了网络对不同尺度信息的提取能力;最后使用RT-DETR的Decoder替换YOLOv8的头部网络,这使得网络模型无需依赖于复杂的NMS后处理步骤,大大简化了检测流程并提高了效率。实验结果表明:改进后的算法模型在自制的烟盒缺陷数据集上与YOLOv8算法相比检测精度提升到了94.6%,检测速度达到了121.4 FPS。并且与其他目标检测算法相比,改进后的算法在检测精度和检测速度方面有一定的优势,更适合应用在烟厂对烟盒表面质量的检测。

In recent years,there has been an increasing demand for higher quality cigarette pack packaging.While modern production has significantly increased the speed of cigarette box production and made production equipment more intelligent,surface quality inspection of cigarette boxes still relies on manual methods.Addressing the issues of human error such as missed or incorrect detections in surface defect inspection,a cigarette box defect detection algorithm based on improved YOLOv8 is proposed.Firstly,a Gather-and-Distribute mechanism is introduced into the neck network of YOLOv8 to enhance the model′s fusion capability for information across different hierarchies.Secondly,a scale sequence feature fusion module is incorporated to strengthen the network′s ability to extract information from different scales.Finally,the head network of YOLOv8 is replaced with the Decoder of RT-DETR,eliminating the need for complex post-processing steps such as Non-Maximum Suppression,thereby simplifying the detection process and improving efficiency.Experimental results show that the improved algorithm model achieves a detection accuracy of 94.6%and a detection speed of 121.4 FPS on a self-made cigarette box defect dataset compared to YOLOv8.Moreover,compared with other object detection algorithms,the improved algorithm has certain advantages in terms of detection accuracy and speed,making it more suitable for application in cigarette factories for surface quality inspection of cigarette boxes.