원문정보
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
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