基于深度学习的水下生物目标检测方法综述

Survey of underwater biological object detection methods based on deep learning

水下生物目标识别对水产养殖、濒危生物保护、生态环境监测具有重要意义。综合分析了当前各种深度学习方法在水下生物目标检测中的应用情况。首先介绍了常用的水下生物目标检测数据集;然后,按照两阶段和单阶段对当前常用目标检测方法进行分类、分析和总结,详细阐述了各类检测方法的实际应用状况,并重点对上述各类检测方法优化策略的优势与不足进行了分析和总结;最后,对基于深度学习的水下生物目标检测提出今后的研究重点,为该领域的研究人员提供了资料性的参考依据。

Underwater biological object detection is crucial for aquaculture,endangered species protection,and ecological environment monitoring.This study comprehensively analyzes the applications of various deep learning methods in underwater biological object detection.The commonly used underwater biological object detection datasets are introduced.The state-of-the-art underwater biological object detection methods are classified,analyzed,and summarized by two stages and one stage.The actual applications of various detection methods are thoroughly described,and the advantages and disadvantages of their optimization strategies are analyzed and summarized.Future works in the field of underwater biological object detection based on deep learning are presented.This study provides a reference basis for researchers in the field of underwater biological object detection.