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Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN
| Content Provider | MDPI |
|---|---|
| Author | Yang, Shihao Jiao, Dongmei Wang, Tongkun He, Yan |
| Copyright Year | 2022 |
| Description | With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects. |
| Starting Page | 3907 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22103907 |
| Journal | Sensors |
| Issue Number | 10 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-21 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Computer Vision Deep Learning Object Detection Tire Defect Detection Feature Pyramid Network |
| Content Type | Text |
| Resource Type | Article |
