沈阳航空航天大学学报2026，Vol.43Issue(1)：48-55,8.DOI:10.3969/j.issn.2095-1248.2026.01.007
基于GCNet网络的玻璃瓶缺陷检测算法
Defect detection algorithm for glass bottles based on GCNet network
摘要
Abstract
Aiming at the problems of high computational cost,large parameter volume,and difficult deployment in current deep learning models for glass bottle defect detection,an efficient lightweight solution was explored.To solve this problem,the feature extraction network GCNet was designed by combining the network structure of YOLOv8.First,GhostConv was used to replace standard convolution.In order to reduce the number of parameters and calculation amount of YOLOv8 bottleneck layer,the convolution module of bottleneck layer was designed,and the bottleneck layer was rebuilt.A new CM module was built based on the structure design of C2f module.The new feature extraction network had a lower parameter count than the original YOLOv8 network.In the feature fusion part,a reconstructed double-weighted bidirectional feature fusion pyramid structure was used to solve the problem of feature information loss with the deepening of network layers.At the same time,for the boundary box regression problem,the combination of WIoU and Inner-ShapeIoU improved the regression convergence speed of the model.The results show that compared with the YOLOv8 algorithm,the YOLOv8-DB composed of the above,the number of parameters is reduced by 45.8%,the calculation amount is reduced by 11.9%,and the accuracy is increased by 0.4%.The improved model can effectively reduce the consumption of computing resources,and is better suitable for specific industrial detection environments.关键词
深度学习/缺陷检测/多尺度卷积/WIoU/轻量化Key words
deep learning/defect detection/multi-scale convolution/WIoU/light weight分类
信息技术与安全科学引用本文复制引用
傅莉,房志磊,席剑辉,任艳..基于GCNet网络的玻璃瓶缺陷检测算法[J].沈阳航空航天大学学报,2026,43(1):48-55,8.基金项目
国家自然科学基金(项目编号:61602321). （项目编号:61602321）
