Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model
As one of the state-of-the-art object detection algorithms, YOLOv5 relies heavily on the quality of the training dataset. In order to improve the detection accuracy and performance of YOLOv5 and to reduce its false positive and false negative rates, we propose to improve the Segment Anything Model (...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/5/1819 |