# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Datasets for Target Detection and Recognition in Turbid Water  
Due to the uniqueness of turbid waters, underwater image datasets are scarce, and have high acquisition costs. Rich and open-source datasets support deep learning theory and effective application, so greater attention should be given to the collection of underwater image datasets in the future. The Open Image Dataset [55], as a composite dataset, contains about 9 million images spanning about 6000 categories and contains more real-life entities than ImageNet. It contains a significant number of aquatic images and underwater image data, which provide sufficient training data for training depth learning network models. [PERSON] et al. [56] introduced an extended underwater image database for salient target detection or salient detection. This dataset is called the Marine Underwater Environment Database (MUED), and contains 8600 underwater images with 430 sets of significant targets. These images have complex backgrounds, many targets, and complex changes in attitude, space position, light, water turbidity, etc. This database also includes manual annotation of truthful information to study more robust underwater image processing and underwater computer vision methods. It can be used to evaluate the performance of target detection and recognition technology in turbid waters, and benefits the development of underwater vision technology, thereby providing unprecedented opportunities for underwater vision and other researchers. Some examples of MUED are displayed in Figure 6.  
Figure 6: Example of MUED. The Brackish dataset [57] is a real underwater biological video dataset, captured 9 m from the water surface at the bottom of Limfjorden, Denmark. This dataset divides videos by frame into more than 14,000 images with a size of 960 \(\times\) 540. It contains more than 11,000 images, including targets to be detected, and more than 3000 images with undetected targets. The entire dataset consists of six categories: big fish, small fish, crab, shrimp, and jellyfish, as depicted in Figure 7.  
The uniqueness of the dataset is that it contains many small aquatic organisms, especially small fish and crabs. The distribution of small fish is centralized, and the dataset also contains some incomplete images of detected targets. The Underwater Image Enhancement Benchmark (UIEB) [58] includes 950 real underwater images, of which 890 have corresponding reference images, and the other 60 underwater images that do not have better reference images are used as challenging data. Using this dataset, the most advanced underwater image enhancement algorithms can be comprehensively studied, which is suitable for training CNN. The Stereo Quantitative Underwater Image Dataset (SQUID) [59] is an image dataset that contains images taken at various locations, with different water properties, and displays color charts in the scene. This dataset can quantitatively evaluate the restoration algorithm of natural images. The Heron Island Coral Reef Dataset (HICRD) [60] is a large real underwater image dataset for underwater image restoration. It contains 2000 reference restored images and 6003 original underwater images in the unpaired training set. A summery of the datasets described in this section is provided in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Main Content** & **Analysis** \\ \hline Open Images Dataset [55] https: & & \\ //github.com/openimages/dataset & Comprehensive dataset & Open source, diversity, wide extending, \\ (accessed on 30 January 2022) & &  suitable for multi-class classifiers. \\ \hline MUED Dataset [56] [[https://zenodo.org/record/2542305#.Ynd05](https://zenodo.org/record/2542305#.Ynd05) YxBxEZ]([https://zenodo.org/record/2542305#.Ynd05](https://zenodo.org/record/2542305#.Ynd05) YxBxEZ) & aquatic organisms image set & Complex background with large number of targets, which is suitable for the \\ (accessed on 30 January 2022) & & high-order training and the verification of \\ \hline \hline \end{tabular}
\end{table}