Underwater object detection technology is crucial in many marine-related fields, including marine environmental monitoring, marine resource development, and marine ecological protection. However, this technology faces great challenges due to the poor quality of underwater optical images and the varying sizes of underwater objects. Therefore, we proposed an underwater optical detection network (UODN) based on the you only look once version 8 (YOLOv8) framework, which addresses these issues through the cross stage multi-branch (CSMB) module and large kernel spatial pyramid (LKSP) module. The aim of the CSMB module is to extract more features from underwater optical images to address the issue of poor image quality, while the LKSP module is designed to enhance the ability of the network to detect underwater objects of various scales. Furthermore, CSMBDarknet built by CSMB and LKSP can be used as the backbone of other underwater object detection algorithms for underwater feature extraction. Extensive experimental results on the underwater robot professional contest 2020 dataset revealed that the average precision (AP) of UODN increased by 1.0%, the AP of UODN increased by 1.1%, and the AP of UODN increased by 2.1% compared with those of the original YOLOv8s. Furthermore, UODN outperforms 12 state-of-the-art models on multiple underwater optical datasets, paving the way for future real-time and high-precision underwater object detection.

中文翻译：

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基于深度学习的复杂水下环境实时水下物体检测技术


水下物体检测技术在海洋环境监测、海洋资源开发、海洋生态保护等许多海洋相关领域至关重要。然而，由于水下光学图像质量较差、水下物体尺寸各异，该技术面临巨大挑战。因此，我们提出了一种基于you only Look Once version 8（YOLOv8）框架的水下光学检测网络（UODN），通过跨阶段多分支（CSMB）模块和大内核空间金字塔（LKSP）模块解决了这些问题。 CSMB模块的目的是从水下​​光学图像中提取更多特征，以解决图像质量差的问题，而LKSP模块的目的是增强网络检测各种尺度水下物体的能力。此外，由 CSMB 和 LKSP 构建的 CSMBDarknet 可以用作其他水下目标检测算法的骨干，用于水下特征提取。在2020年水下机器人专业大赛数据集上的大量实验结果显示，UODN的平均精度（AP）较原始提高了1.0%，UODN的AP提高了1.1%，UODN的AP提高了2.1% YOLOv8s。此外，UODN 在多个水下光学数据集上的性能优于 12 个最先进的模型，为未来实时、高精度水下物体检测铺平了道路。