河南科技大学学报（自然科学版）2025，Vol.46Issue(5)：40-50,11.DOI:10.15926/j.cnki.issn1672-6871.2025.05.006
基于轻量级高分辨率网络的金属产品质量检测方法
A Lightweight High-Resolution Network-Based Method for Metal Product Quality Inspection
摘要
Abstract
Traditional manual inspection suffers from issues such as inconsistent standards and susceptibility to fatigue interference.However,existing deep learning methods based on real-time object detection algorithms tend to lose feature details due to downsampling operations and exhibit high computational complexity,making it difficult to meet precise detection requirements.To address these challenges,this paper proposes a lightweight high-resolution network detection method for metal surface defects.This method preserves subtle defect signals through a full-process high-resolution feature preservation architecture,achieves a lightweight design by integrating depthwise separable convolutions,introduces a conditional channel weighting mechanism to optimize multi-resolution feature fusion,and proposes a global spatial feature extraction method to enhance contextual correlation.Experiments show that the network achieves mAP50 scores of 79.6％and 70.9％on the NEU-DET and GC10-DET datasets,respectively,with a parameter count of only 5.3M and 7.1 GFLOPs.When tested on a dual RTX 4090 GPU setup,it reaches a frame rate of 117 FPS.Compared with previous benchmark models,the proposed method demonstrates significant advantages in both detection accuracy and computational efficiency.Ablation experiments verify the effectiveness of each module,providing a high-precision and low-power solution for real-time industrial detection of metal surface defects.关键词
智能制造/模式识别/金属表面缺陷/表面质量检测/轻量化模型Key words
intelligent manufacturing/pattern recognition/metal surface defect/surface quality inspection/lightweight model分类
信息技术与安全科学引用本文复制引用
韩俊嘉,段成璞,黄景涛..基于轻量级高分辨率网络的金属产品质量检测方法[J].河南科技大学学报（自然科学版）,2025,46(5):40-50,11.基金项目
国家自然科学基金项目(U1504617) （U1504617）
