大连理工大学学报2026，Vol.66Issue(1)：86-93,8.DOI:10.7511/dllgxb202601011
基于多特征融合的集装箱船导轨缺陷检测算法
Container ship guide rail defect detection algorithm based on multi-feature fusion
摘要
Abstract
In response to the issues of traditional container ship guide rail defect detection methods,which rely entirely on manual visual inspection and suffer from low efficiency and heavy workloads,a defect detection algorithm is proposed for container ship guide rails based on multi-feature fusion.A processing method of data adaptive resampling is designed to mitigate the impact of uneven distribution of defect types.A multi-gradient receptive field aggregation module is incorporated into the backbone network to aggregate features of guide rails with varying degrees of damage and their surrounding environmental features.Building on this approach,a hybrid attention mechanism is embedded after the residual analysis module to effectively guide multi-scale feature flows toward key feature information.At the feature concatenation points of the network,a feature reorganization upsampling operator is fused to expand the local receptive field of incoming features and effectively integrate global subtle feature information.Validation on the test set and comparison with manual efficiency demonstrate that the proposed improved algorithm achieves a mean average precision of 97.0％for guide rail defect detection,which is 2.9 percentage points higher than the original YOLOv5 algorithm,effectively enhancing the detection accuracy of container ship guide rail defects.关键词
船舶建造工艺/集装箱船导轨缺陷/混合注意力机制/特征重组上采样算子Key words
shipbuilding process/container ship guide rail defects/hybrid attention mechanism/feature reorganization upsampling operator分类
交通工程引用本文复制引用
李瑞,张贤宇,尤尹,汪骥,张全有..基于多特征融合的集装箱船导轨缺陷检测算法[J].大连理工大学学报,2026,66(1):86-93,8.基金项目
国家自然科学基金资助项目(51979033). （51979033）
