DUO

Aiming at the problem of low underwater recognition accuracy caused by dense and fuzzy targets in underwater target detection, an underwater target detection algorithm combining attention mechanism and downsampling is proposed.For the experimental evaluation, the Detecting Underwater Objects (DUO) dataset was used, The dataset was de-duplicated using a perceptual hash algorithm, of which 6671 sheets were used for training, 1111 sheets for testing, and 1111 sheets for validation. There are only a few similar images in the new dataset. Additionally, the DUO dataset includes some images from various underwater scene datasets.The total number of targets in the dataset is 74,515, of which the number of holothurians, echinus, scallops, and starfish are 7,887, 50,156, 1,924, and 14,548, respectively.Image tag has been converted from json file to txt file, which is convenient for yolo algorithm.

针对水下目标检测任务中目标密集且模糊所导致的识别精度偏低问题，本文提出一种融合注意力机制（attention mechanism）与下采样（downsampling）的水下目标检测算法。为完成实验评估，本研究采用了水下目标检测（Detecting Underwater Objects, DUO）数据集。该数据集通过感知哈希算法（perceptual hash algorithm）完成去重处理，其中6671张图像用于训练集，1111张用于测试集，1111张用于验证集。经去重处理后的新数据集仅包含少量相似图像。此外，DUO数据集还收录了多类水下场景数据集的图像。该数据集的目标总数量为74515个，其中海参、海胆、扇贝及海星的数量分别为7887、50156、1924及14548。图像标签已从JSON文件转换为TXT文件，以适配YOLO算法的使用需求。

搜集汇总

背景与挑战

背景概述

DUO数据集是一个专注于水下目标检测的数据集，旨在解决因目标密集和模糊导致的识别准确率低的问题。数据集包含7,887张海参、50,156张海胆、1,924张扇贝和14,548张海星的图像，总目标数量为74,515，适用于YOLO算法。