铸造2024，Vol.73Issue(6)：843-851,9.
基于双向加权特征融合网络的铸件内部缺陷检测方法
Casting Internal Defect Detection Method Based on Bidirectional Weighted Feature Fusion Network
摘要
Abstract
Aiming at the problems of small internal defects,weak contrast and low efficiency of manual recognition in the process of X-ray nondestructive testing,a method of casting internal defects detection based on bi-weighted feature fusion network was proposed.Based on the YOLOv5 network model,an improved coordinate attention module(NCA)was introduced to improve the learning ability of the network for irregular defects and minor defects.Bidirectional feature pyramid network(BiFPN)was introduced to replace the original path aggregation network(PANet)to achieve multi-scale efficient fusion of defect features,and EIoU Loss regression loss function was used to improve the accuracy of defect boundary frame location.The experimental results showed that the proposed method had good performance in detecting small targets and weak contrast defects in the castings.关键词
铸件/缺陷检测/深度学习/注意力模块/双向加权特征融合Key words
castings/defect detection/deep learning/attention module/bidirectional weighted feature fusion分类
矿业与冶金引用本文复制引用
王蕾,贺万山,张泽琳,夏绪辉..基于双向加权特征融合网络的铸件内部缺陷检测方法[J].铸造,2024,73(6):843-851,9.基金项目
国家自然科学基金面上项目(52275503) （52275503）
湖北省重点研发计划项目(2022BAD102,2023BAB048). （2022BAD102,2023BAB048）
