Pavement crack detection method based on improved YOLOv5
Aiming at the problem that the existing crack detection model is large in size and the detection accuracy is not high, this paper proposes a crack detection method for UAV aerial images based on lightweight network. Firstly, the MobileNetv3 network is used instead of the YOLOv5 backbone network to r...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2024-03-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000164120 |
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| Summary: | Aiming at the problem that the existing crack detection model is large in size and the detection accuracy is not high, this paper proposes a crack detection method for UAV aerial images based on lightweight network. Firstly, the MobileNetv3 network is used instead of the YOLOv5 backbone network to reduce the model size. Secondly, the C3TR and CBAM modules are introduced to improve the network characterization ability, and the loss function is replaced with EIOU to improve the robustness of the model. Experimental results show that the proposed method obtains 98.9% accuracy on the self-made dataset, which is 1.2% higher than the original YOLOv5, the model size is reduced by 51.5%, and the detection speed is increased by 37%. The improved model is superior to four common crack detection models such as Faster-RCNN in terms of accuracy, size and speed, which meets the real-time, lightweight and accuracy requirements of crack detection. |
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| ISSN: | 0258-7998 |