机械与电子2026，Vol.44Issue(4)：62-67,6.
基于EfficientNetB3与嵌入式系统的热轧钢带表面缺陷分拣系统
A Surface Defect Sorting System for Hot-rolled Steel Strip Based on EfficientNetB3 and Embedded System
摘要
Abstract
To address the issues of low efficiency in manual detection and poor generalization capability of traditional machine vision methods for surface defects on steel strips in industrial scenarios,an automa-ted sorting system that integrates deep learning with embedded control is proposed.At the algorithmic lev-el,a transfer learning model based on the EfficientNetB3 backbone is constructed and optimized via a two-stage fine-tuning strategy.This model achieves an average classification precision of 0.99 on the NEU-DET dataset,with a single-image inference time of approximately 58 ms,satisfying industrial real-time requirements.At the system level,a collaborative hardware platform comprising an upper computer(deci-sion-making)and a lower computer(execution)was designed and implemented,integrating multiple functional modules such as image acquisition,defect recognition,and mechanical sorting.Integrated test re-sults demonstrate that the system achieves an overall sorting accuracy of 97.8%for six typical defect types:patches,rolled-in scale,cracks,scratches,pitted surfaces,and inclusions.This effectively validates its application potential and practical value for achieving high-precision and high-efficiency automated quality inspection in industrial settings.关键词
表面缺陷检测/EfficientNetB3/嵌入式系统/实时分拣Key words
surface defect detection/EfficientNetB3/embedded system/real-time sorting分类
信息技术与安全科学引用本文复制引用
蔡盼盼,刘娟,鲁忠臣..基于EfficientNetB3与嵌入式系统的热轧钢带表面缺陷分拣系统[J].机械与电子,2026,44(4):62-67,6.基金项目
2025年度广东省本科高校教学质量与教学改革工程建设项目(粤教高函[2026]4号) （粤教高函[2026]4号）
