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.

针对水下目标检测中目标密集模糊导致识别精度低下的问题，本文提出了一种融合注意力机制与下采样的水下目标检测算法。为开展实验评估，本研究采用了水下目标检测数据集（Detecting Underwater Objects，DUO）。该数据集已通过感知哈希算法完成去重处理，其中6671张图像用于训练，1111张用于测试，1111张用于验证。经去重后的新数据集仅包含少量相似图像。此外，DUO数据集还收录了来自多个水下场景数据集的图像。该数据集总目标数量为74515个，其中海参、海胆、扇贝与海星的数量分别为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是一个专为水下目标检测设计的数据集，旨在通过结合注意力机制和下采样算法解决水下目标密集和模糊导致的识别准确率低的问题。该数据集包含去重后的图像，分为训练集6671张、测试集1111张和验证集1111张，总目标数74,515个，涵盖海参、海胆、扇贝和海星四类目标，且标签已转换为TXT格式以适配YOLO算法。