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A lightweight YOLOv8 integrating FasterNet for real-time underwater object detection

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Abstract

In this paper, we propose a underwater target detection method that optimizes YOLOv8s to make it more suitable for real-time and underwater environments. First, a lightweight FasterNet module replaces the original backbone of YOLOv8s to reduce the computation and improve the performance of the network. Second, we modify current bi-directional feature pyramid network into a fast one by reducing unnecessary feature layers and changing the fusion method. Finally, we propose a lightweight-C2f structure by replacing the last standard convolution, bottleneck module of C2f with a GSConv and a partial convolution, respectively, to obtain a lighter and faster block. Experiments on three underwater datasets, RUOD, UTDAC2020 and URPC2022 show that the proposed method has mAP\(_{50}\) of 86.8%, 84.3% and 84.7% for the three datasets, respectively, with a speed of 156 FPS on NVIDIA A30 GPUs, which meets the requirement of real-time detection. Compared to the YOLOv8s model, the model volume is reduced on average by 24%, and the mAP accuracy is enhanced on all three datasets.

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Data availability

The data that support the findings of this study are openly available in RUOD, UTDAC2020, and URPC2022, at https://github.com/dlut-dimt/RUOD, https://aistudio.baidu.com/aistudio/datasetdetail/215376, and https://openi.pcl.ac.cn/OpenOrcinus_orca/URPC2022_Acoustic_Solution, respectively.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China

    An Guo, Kaiqiong Sun & Ziyi Zhang

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  1. An Guo
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Correspondence to Kaiqiong Sun.

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Guo, A., Sun, K. & Zhang, Z. A lightweight YOLOv8 integrating FasterNet for real-time underwater object detection. J Real-Time Image Proc 21, 49 (2024). https://doi.org/10.1007/s11554-024-01431-x

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