# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 5 Applications of Underwater Target Detection and Recognition Technology
### Target Detection and Recognition of Underwater Organisms  
Target detection technology is used to monitor fish and sea cucumbers in aquaculture, most of which use deep learning methods for detection. [PERSON] [62] developed a fish channel observation platform, which combines the convolutional neural network and Kalman filter to realize automatic detection of fish species and quantities using sonar and optical cameras. [PERSON] [63] proposed a method for identifying the unique behaviors of fish schools based on simulated feature point selection (SFPS). By combining feature point extraction with special behavior recognition, the detection accuracy of feeding behaviors was 96.02%.  
[PERSON] [64] proposed an underwater sea cucumber image enhancement method based on the fusion of the Retinex algorithm and dark channel prior algorithm. Using the evaluation indicators of the MSE, equivalent numbers of looks (ENL), information entropy (IE), and signal to noise ratio (SNR) values, this method has shown an advantageous effect regarding defogging and the enhancement of underwater sea cucumber images. [PERSON] [65] performed sea cucumber detection based on a stochastic gradient descent algorithm, and the developed recognition model performed well on various forms of obstacles and natural sites. [PERSON] [66] proposed a sea cucumber detection method based on Faster R-CNN and plotted the motion trajectories of sea cucumbers. The experimental results showed that this method enabled accurate identification and localization of sea cucumbers. Analysis of the behavioral patterns of sea cucumbers through deep learning provides valuable information for the health status assessment and early identification of diseases in sea cucumber farming.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Main Content** & **Analysis** \\ \hline [PERSON] et al. [61] https: & & \\ //github.com/agaldran/UnderWater & underwater biological image set &
\begin{tabular}{c} Includes some species of \\ underwater organisms. \\ \end{tabular} \\ (accessed on 3 February 2022) & & \\ \hline SQUID Dataset [59] https: & & \\ //paperswithcode.com/dataset/squid & & \\ (accessed on 3 February 2022) & & \\ \hline Brackish dataset [57] & & \\ [[https://www.kaggle.com/](https://www.kaggle.com/)]([https://www.kaggle.com/](https://www.kaggle.com/)) & & \\ aalborguniversity/brackish-dataset & & \\ (accessed on 2 February 2022) & & \\ \hline HICRD Dataset [60] https: & & \\ //paperswithcode.com/dataset/hicrd & & \\ (accessed on 3 February 2022) & & \\ \hline UIEB Dataset [58] https://li-chongyi. & & \\ github.io/proj\_benchmark.html & & \\ (accessed on 2 February 2022) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._[PERSON] [67] proposed a real-time lightweight multi-scale object detection algorithm called Faster MSSDLite, which was used to detect live crabs in underwater images. Its noise reduction and detection effects were found to be very efficient, and it is suitable for low-performance embedded equipment, such as automatic feeding boats. [PERSON] [68] used image dehazing technology and the YOLO algorithm to identify and track marine life, including sharks, crabs, etc. [PERSON] [69] proposed a deep convolutional neural network architecture for scallop detection, using the YOLOv2 algorithm to make the system run faster and achieve high accuracy. This network can be used on autonomous underwater vehicles (AUVs) for real-time scallop population counting and health monitoring.