YOLO-LRDD: a lightweight method for road damage detection based on improved YOLOv5s
Abstract In computer vision, timely and accurate execution of object identification tasks is critical. However, present road damage detection approaches based on deep learning suffer from complex models and computationally time-consuming issues. To address these issues, we present a lightweight mode...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-10-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-022-00931-x |