| Autor: |
Shufang Xu, Hanqing Tang, Jianni Li, Longbao Wang, Xuejie Zhang, Hongmin Gao |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 13, Iss 15, p 8890 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app13158890 |
| Popis: |
Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model’s performance. Specifically, the SPDCS module retains all information in the channel dimension, thereby enhancing the model’s detection accuracy and recognition ability for water-crossing objects. The SPPAUG module performs multiscale feature fusion, which further improves the model’s detection accuracy and recognition ability. Moreover, the C2f module is introduced from YOLOv8 to increase the detection speed. Experimental results on a water-floating object dataset demonstrate that the improved YOLOW model outperforms the standard YOLOv5s algorithm, especially in water-crossing object detection. This research has significant implications for environmental monitoring and protection. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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