LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach
Underwater object detection plays a significant role in fisheries resource assessment and ecological environment protection. However, traditional underwater object detection methods struggle to achieve accurate detection in complex underwater environments with limited computational resources. This p...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1513740/full |
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| author | Mingxin Liu Mingxin Liu Yujie Wu Ruixin Li Cong Lin Cong Lin |
| author_facet | Mingxin Liu Mingxin Liu Yujie Wu Ruixin Li Cong Lin Cong Lin |
| author_sort | Mingxin Liu |
| collection | DOAJ |
| description | Underwater object detection plays a significant role in fisheries resource assessment and ecological environment protection. However, traditional underwater object detection methods struggle to achieve accurate detection in complex underwater environments with limited computational resources. This paper proposes a lightweight underwater object detection network called LightFusionNet-YOLO (LFN-YOLO). First, we introduce the reparameterization technique RepGhost to reduce the number of parameters while enhancing training and inference efficiency. This approach effectively minimizes precision loss even with a lightweight backbone network. Then, we replaced the standard depthwise convolution in the feature extraction network with SPD-Conv, which includes an additional pooling layer to mitigate detail loss. This modification effectively enhances the detection performance for small objects. Furthermore, We employed the Generalized Feature Pyramid Network (GFPN) for feature fusion in the network's neck, enhancing the network's adaptability to features of varying scales. Finally, we design a new detection head, CLLAHead, which reduces computational costs and strengthens the robustness of the model through cross-layer local attention. At the same time, the DFL loss function is introduced to reduce regression and classification errors. Experiments conducted on public datasets, including URPC, Brackish, and TrashCan, showed that the mAP@0.5 reached 74.1%, 97.5%, and 66.2%, respectively, with parameter sizes and computational complexities of 2.7M and 7.2 GFLOPs, and the model size is only 5.9 Mb. Compared to mainstream vision models, our model demonstrates superior performance. Additionally, deployment on the NVIDIA Jetson AGX Orin edge computing device confirms its high real-time performance and suitability for underwater applications, further showcasing the exceptional capabilities of LFN-YOLO. |
| format | Article |
| id | doaj-art-e9808efa81d04b5d8446fc5769dd1e84 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-e9808efa81d04b5d8446fc5769dd1e842025-01-23T05:10:29ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011110.3389/fmars.2024.15137401513740LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approachMingxin Liu0Mingxin Liu1Yujie Wu2Ruixin Li3Cong Lin4Cong Lin5School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaGuangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang, ChinaCollege of Naval Architecture and Shipping, Guangdong Ocean University, Zhanjiang, ChinaCollege of Naval Architecture and Shipping, Guangdong Ocean University, Zhanjiang, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, ChinaGuangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang, ChinaUnderwater object detection plays a significant role in fisheries resource assessment and ecological environment protection. However, traditional underwater object detection methods struggle to achieve accurate detection in complex underwater environments with limited computational resources. This paper proposes a lightweight underwater object detection network called LightFusionNet-YOLO (LFN-YOLO). First, we introduce the reparameterization technique RepGhost to reduce the number of parameters while enhancing training and inference efficiency. This approach effectively minimizes precision loss even with a lightweight backbone network. Then, we replaced the standard depthwise convolution in the feature extraction network with SPD-Conv, which includes an additional pooling layer to mitigate detail loss. This modification effectively enhances the detection performance for small objects. Furthermore, We employed the Generalized Feature Pyramid Network (GFPN) for feature fusion in the network's neck, enhancing the network's adaptability to features of varying scales. Finally, we design a new detection head, CLLAHead, which reduces computational costs and strengthens the robustness of the model through cross-layer local attention. At the same time, the DFL loss function is introduced to reduce regression and classification errors. Experiments conducted on public datasets, including URPC, Brackish, and TrashCan, showed that the mAP@0.5 reached 74.1%, 97.5%, and 66.2%, respectively, with parameter sizes and computational complexities of 2.7M and 7.2 GFLOPs, and the model size is only 5.9 Mb. Compared to mainstream vision models, our model demonstrates superior performance. Additionally, deployment on the NVIDIA Jetson AGX Orin edge computing device confirms its high real-time performance and suitability for underwater applications, further showcasing the exceptional capabilities of LFN-YOLO.https://www.frontiersin.org/articles/10.3389/fmars.2024.1513740/fullunderwater object detectionlightweight detectorsmall objectmarine resourcesmulti-scale feature fusion |
| spellingShingle | Mingxin Liu Mingxin Liu Yujie Wu Ruixin Li Cong Lin Cong Lin LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach Frontiers in Marine Science underwater object detection lightweight detector small object marine resources multi-scale feature fusion |
| title | LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach |
| title_full | LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach |
| title_fullStr | LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach |
| title_full_unstemmed | LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach |
| title_short | LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach |
| title_sort | lfn yolo precision underwater small object detection via a lightweight reparameterized approach |
| topic | underwater object detection lightweight detector small object marine resources multi-scale feature fusion |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1513740/full |
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