摘要
To address the problems of large number of parameters and high complexity of calculation in the current steel surface defect detection model,a steel surface defect lightweight algorithm CGV-YOLO based on the improvement of YOLOv8 was proposed in this study.Firstly,in the process of optimizing the network architecture,the algorithm designed the FRC module and embeds it in the backbone network.Then,the GSConv convolution was employed to construct the Slim-neck network architecture,which further reduces computational load while maintaining model accuracy.Finally,the optimized CBAM replaced the C2f module in the YOLOv8 backbone network,reducing both model parameters and computational load.Based on the database of NEU-DET and BSD,the F1-Score of the CGV-YOLO algorithm is improved by 1.3%and 1.1%respectively compared with the baseline model.Based on the database of NEU-DET,the Params and computational complexity of the model are reduced by 30.6%and 35.3%respectively against the baseline.The results demonstrated that the proposed algorithm drastically reduces the number of parameters and computational cost with the maintenance of the accuracy of the model and realizes the lightweight effect.
针对目前钢材表面缺陷检测模型存在参数量大和计算复杂度高的问题,本研究提出一种基于改进YOLOv8的钢材表面缺陷轻量化算法CGV-YOLO。首先该算法在优化网络架构的过程中,设计了FRC模块并嵌入主干网络;接着使用GSConv卷积搭建Slim-neck颈部网络结构,在保证模型准确性前提下进一步降低计算量;最后,将优化后的CBAM替换YOLOv8主干网络的C2f模块,减少模型参数和计算量。在NEU-DET和BSD数据集上,CGV-YOLO算法的F1_Score指标较基线模型分别提升1.3%和1.1%。在NEU-DET数据集上,模型的参数量和计算量较基线模型分别下降30.6%和35.3%,这表明算法在保持了模型精度的同时较大幅度降低了参数量和计算量,实现了轻量化效果。
出处
《印刷与数字媒体技术研究》
2025年第6期59-67,共9页
Printing and Digital Media Technology Study
基金
国家自然科学基金委员会(No.52378254)。
