Underwater Object Detection Algorithm Based on an Improved YOLOv8
Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the...
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/1991 |
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| _version_ | 1846153280505249792 |
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| author | Fubin Zhang Weiye Cao Jian Gao Shubing Liu Chenyang Li Kun Song Hongwei Wang |
| author_facet | Fubin Zhang Weiye Cao Jian Gao Shubing Liu Chenyang Li Kun Song Hongwei Wang |
| author_sort | Fubin Zhang |
| collection | DOAJ |
| description | Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. This improvement increases the capability of detecting multi-scale objects in complex underwater environments without adding a computational burden. Next, the incorporation of a Partial Self-Attention (PSA) module at the end of the backbone network enhances model efficiency and optimizes the utilization of computational resources while maintaining high performance. Finally, the integration of the Neck component from the Gold-YOLO model improves the neck structure of the YOLOv8 model, facilitating the fusion and distribution of information across different levels, thereby achieving more efficient information integration and interaction. Experimental results show that YOLOv8-CPG significantly outperforms the traditional YOLOv8 in underwater environments. Precision and Recall show improvements of 2.76% and 2.06%. Additionally, mAP50 and mAP50-95 metrics have increased by 1.05% and 3.55%, respectively. Our approach provides an efficient solution to the difficulties encountered in underwater object detection. |
| format | Article |
| id | doaj-art-44577d0fef224eb6afb03ddb885878b7 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-44577d0fef224eb6afb03ddb885878b72024-11-26T18:08:11ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211199110.3390/jmse12111991Underwater Object Detection Algorithm Based on an Improved YOLOv8Fubin Zhang0Weiye Cao1Jian Gao2Shubing Liu3Chenyang Li4Kun Song5Hongwei Wang6School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaDue to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. This improvement increases the capability of detecting multi-scale objects in complex underwater environments without adding a computational burden. Next, the incorporation of a Partial Self-Attention (PSA) module at the end of the backbone network enhances model efficiency and optimizes the utilization of computational resources while maintaining high performance. Finally, the integration of the Neck component from the Gold-YOLO model improves the neck structure of the YOLOv8 model, facilitating the fusion and distribution of information across different levels, thereby achieving more efficient information integration and interaction. Experimental results show that YOLOv8-CPG significantly outperforms the traditional YOLOv8 in underwater environments. Precision and Recall show improvements of 2.76% and 2.06%. Additionally, mAP50 and mAP50-95 metrics have increased by 1.05% and 3.55%, respectively. Our approach provides an efficient solution to the difficulties encountered in underwater object detection.https://www.mdpi.com/2077-1312/12/11/1991underwater recognitionYOLOv8object detection |
| spellingShingle | Fubin Zhang Weiye Cao Jian Gao Shubing Liu Chenyang Li Kun Song Hongwei Wang Underwater Object Detection Algorithm Based on an Improved YOLOv8 Journal of Marine Science and Engineering underwater recognition YOLOv8 object detection |
| title | Underwater Object Detection Algorithm Based on an Improved YOLOv8 |
| title_full | Underwater Object Detection Algorithm Based on an Improved YOLOv8 |
| title_fullStr | Underwater Object Detection Algorithm Based on an Improved YOLOv8 |
| title_full_unstemmed | Underwater Object Detection Algorithm Based on an Improved YOLOv8 |
| title_short | Underwater Object Detection Algorithm Based on an Improved YOLOv8 |
| title_sort | underwater object detection algorithm based on an improved yolov8 |
| topic | underwater recognition YOLOv8 object detection |
| url | https://www.mdpi.com/2077-1312/12/11/1991 |
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