基于EfficientNetB3与嵌入式系统的热轧钢带表面缺陷分拣系统OA

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.