Earticle

초록

영어

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

목차

Abstract
1. Introduction
2. Related Works
2.1 Traditional Methods
2.2 Deep Learning Methods
3. Methodology
3.1 One-Stage versus Two-Stage Detector
3.2 Our Defect Detection Model
4. Experiments
4.1 Datasets
4.2 Performance Evaluation
4.3 Losses Evaluation
4.4 Comparison of Accuracy with Deep Learning Methods
4.5 Comparison of Accuracy with Traditional Methods
4.6 Comparison of Time Factor between One-Stage and Two-Stage Detectors
5. Conclusion
References

키워드

저자

• Mansi Sharma [ Ph.D. Candidate, Department of Computer Engineering, Kongju National University, Korea ]
• Jong-Tae Lim [ Professor, Department of Artificial Intelligence, Kongju National University, Korea ]
• Yi-Geun Chae [ Associate Professor, Department of Computer Engineering, Kongju National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380