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Surface Defect Detection of Strip-Steel Based on an Improved PP-YOLOE-m Detection Network
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Yang Liu, Xiaofang Guo, Jun Zhou, Pengcheng |
| Copyright Year | 2022 |
| Description | Surface-defect detection is crucial for assuring the quality of strip-steel manufacturing. Strip-steel surface-defect detection requires defect classification and precision localization, which is a challenge in real-world applications. In this research, we propose an improved PP-YOLOE-m network for detecting strip-steel surface defects. First, data augmentation is performed to avoid the overfitting problem and to improve the model’s capacity for generalization. Secondly, Coordinate Attention is embedded in the CSPRes structure of the backbone network to improve the backbone network’s feature extraction capabilities and obtain more spatial location information. Thirdly, Spatial Pyramid Pooling is specifically replaced for the Atrous Spatial Pyramid Pooling in the neck network, enabling the multi-scale network to broaden its receptive field and gain more information globally. Finally, the SIoU loss function more accurately calculates the regression loss over GIoU. Experimental results show that the improved PP-YOLOE-m network’s AP, AP |
| Starting Page | 2603 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics11162603 |
| Journal | Electronics |
| Issue Number | 16 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-19 |
| Access Restriction | Open |
| Subject Keyword | Electronics Industrial Engineering Defect Detection Data Augmentation Coordinate Attention Atrous Spatial Pyramid Pooling Loss Function Real-time Detection |
| Content Type | Text |
| Resource Type | Article |
