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Related Experiment Video

Updated: Sep 16, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Workpiece surface defect detection based on YOLOv11 and edge computing.

Zishuo Wang1, Tao Ding1, Shuning Liang1

  • 1School of Information and Control Engineering, Jilin Institute of Chemical Technology, JiLin, China.

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|July 9, 2025
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Summary
This summary is machine-generated.

This study introduces YOLOv11 for workpiece surface defect detection, enhancing accuracy and speed by integrating edge computing. The YOLOv11 model significantly improves detection performance on industrial datasets.

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Area of Science:

  • Industrial Automation
  • Computer Vision
  • Machine Learning

Background:

  • Industrial production demands high-quality workpieces, necessitating efficient surface defect detection.
  • Current cloud-based defect detection methods face challenges with large data transmission and reduced speeds.
  • Edge computing offers a solution to overcome computational burdens and improve real-time detection.

Purpose of the Study:

  • To propose an efficient and precise workpiece surface defect detection method using YOLOv11 and edge computing.
  • To enhance the YOLOv11 model's performance through data augmentation and generative adversarial networks.
  • To validate the model's generalizability and deploy it on edge devices for improved detection speed.

Main Methods:

  • Dataset expansion using random flipping, cropping, and self-attention generative adversarial network (SA-GAN).
  • Comparative analysis of YOLOv7 to YOLOv11 models on the NEU-DET and Tianchi aluminium profile surface defect datasets.
  • Conversion of the cloud-based YOLOv11 model to an edge-based YOLOv11-RKNN model for deployment on RK3568 edge devices.

Main Results:

  • YOLOv11 with SA-GAN demonstrated superior mAP@0.5 improvements over YOLOv7, YOLOv8, YOLOv9, and YOLOv10 on the NEU-DET dataset.
  • The model achieved 87.0% mAP@0.5 on the Tianchi dataset, confirming its generalizability.
  • Edge deployment reduced model size by 59.3% and single-image detection time by 35.5% (from 52.1ms to 33.6ms).

Conclusions:

  • The proposed YOLOv11 model, enhanced with SA-GAN, offers significant accuracy improvements for workpiece surface defect detection.
  • Edge deployment of YOLOv11 provides a substantial enhancement in detection speed and efficiency for industrial applications.
  • The findings validate the effectiveness and generalizability of YOLOv11 for real-world industrial defect detection tasks.