铸造技术2025，Vol.46Issue(10)：973-981,9.DOI:10.16410/j.issn1000-8365.2025.5120
面向钢表面缺陷的双模态目标检测方法
Dual-Modal Target Detection Method for Steel Surface Defects
摘要
Abstract
Steel surface defect detection is a core aspect of industrial quality control.Given the insufficient robustness of existing RGB single-modality-based defect detection models,which often suffer from high rates of false positives,false negatives,and incorrect detections of spatial morphological defects,the parallel multi-modal spatial-aware fusion YOLOv8(PMSF-YOLOv8)algorithm was proposed.This algorithm employs a dual-branch heterogeneous network to enhance the learning of RGB texture and depth spatial features.In the mid-fusion stage,the dual-modal feature fusion module(DFFM)was utilized to achieve adaptive fusion of multiscale features through dynamic weights.The NUE-RSDDS-AUG dataset was used for validation.The results show that the PMSF-YOLOv8 network model achieves a detection accuracy of mAP@0.5 of 98.6％,with a false alarm rate reduced by 2.1％compared with that of single-modality methods,striking a balance between "high accuracy and low false alarms".关键词
双模态/钢表面缺陷检测/YOLOv8/特征融合/注意力机制Key words
dual-modality/steel surface defect detection/YOLOv8/feature fusion/attention mechanism分类
计算机与自动化引用本文复制引用
李芹芹,王奎越,宋宝宇,宋君,马晓国..面向钢表面缺陷的双模态目标检测方法[J].铸造技术,2025,46(10):973-981,9.基金项目
国家重点研发计划(2022YFB3304800) （2022YFB3304800）
