重庆邮电大学学报（自然科学版）2026，Vol.38Issue(3)：515-528,14.DOI:10.3979/j.issn.1673-825X.202510100218
面向工业表面缺陷的低复杂度检测网络
Low complexity detection network for industrial surface defects
摘要
Abstract
To address industrial scenarios characterized by"small but numerous defects,complex backgrounds,and limited computing power",this paper proposes the YOLO-DAFE architecture.The framework first designs FD-DConv for frequency-domain decoupling and reweighting to capture subtle defects,then introduces C2f-OREPA by training multi-branches and reparameterizing inference to overcome low FLOPs constraints,and finally combines Focaler and IoU for joint optimization of localization and classification.On the enhanced NEU-DET dataset,YOLO-DAFE achieves 94.3%mAP with only 6.2G FLOPs,reducing computational cost by 1.9G while improving accuracy by 1.8%compared to the baseline.On the generalization dataset GC10-DET,it achieves 1.3 percentage points higher mAP and 1.2 percentage points higher mAP@0.5:0.95 compared to the baseline.Final validation through Jetson Orin Nano Super resource-constrained devices demonstrates that both datasets match PC-level accuracy and maintain the highest mAP50 across all models,showcasing strong adaptability for industrial applications.Additionally,it achieves the fastest inference time under GPU and FP16 modes with quantization degradation<1%,balancing high precision,low latency,and easy deployment to meet real-time industrial defect detection requirements.关键词
钢材缺陷检测/深度学习/YOLO V8/多参数网络/频率动态卷积/重参数化Key words
steel defect detection/deep learning/YOLO V8/multi-parameter network/frequency dynamic convolution/reparameterization分类
信息技术与安全科学引用本文复制引用
周雅洁,朱树先,余鹏敏,韩惟皓..面向工业表面缺陷的低复杂度检测网络[J].重庆邮电大学学报（自然科学版）,2026,38(3):515-528,14.基金项目
国家自然科学基金项目(82170897,81971753) National Natural Science Foundation of China(82170897,81971753) （82170897,81971753）
