# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Target Detection Based on Deep Learning Methods
[20] focused on a one-stage algorithm and proposed a target detection algorithm YOLOv3-brackish based on an improved scale and attention mechanism. In this algorithm, the extrusion excitation module is added behind the deep convolution layer, which enhances the feature extraction ability of the YOLOv3 model. To solve the problem of multiple small targets, the identification of which is difficult, shallow features with greater location information were combined with deep features to improve the detection performance of the small target model. The experimental results showed that the improved YOLOv3-brackish model performed better than SSD, Faster CNN, and YOLOv3, and the effect is demonstrated in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Number of** & **Number of Trainable** & **Training Model** & **Accuracy (\%)** \\  & **Parameters** & **Parameters** & **Storage** & **Accuracy (\%)** \\ \hline ResNet50 [17] & 34,386,842 & 34,356,164 & 137.8 MB & 78.28\% \\ MobileNet [18] & 8,280,256 & 8,255,236 & 33.3 MB & 82.19\% \\ MobileNetV2 [16] & 6,743,096 & 6,701,188 & 27.2 MB & 84.57\% \\ DenseNet [19] & 42,726,388 & 42,672,772 & 171.4 MB & 83.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the experimental results of different basic networks. The networks were pretrained by the ImageNet. Figure 4: Comparison of target detection results. (**a**) Original picture. (**b**) Correct annotation. The target detection result of (**c**) SSD, (**d**) Faster RCNN, (**e**) YOLOv3, and (**f**) YOLOv3-brackish. [PERSON] et al. [21] combined image processing with deep learning to realize species identification and density calculation of marine organisms to monitor the invasion of marine organisms in real-time. An underwater camera was used to capture image data in real-time within the monitoring range and deep learning was used to achieve end-to-end recognition of jellyfish. First, the convolution neural network was designed and improved, and a convolution neural network composed of two convolution layers, two pooling layers, and a full connection layer was obtained. After training, the convolution neural network was predicted using test sample images. Under the non-uniform light field, the characteristics of the biological images taken in turbid water were investigated using image sharpening, edge detection, edge closure, hole filling, etc. A binary image separate from the target and background was obtained, demonstrating real-time estimation of the marine biological density. The results showed that this method can be effectively applied to calculate the marine biological density and detect marine biological species. This study provides a reference for the early warning of biological invasion in offshore waters. [PERSON] et al. [22] focused on the comparison of various existing edge detection methods, including Laplace, SobelX, SobelY, Combined Sobel, and the Canny detector. The results of these methods were obtained and analyzed, as shown in Table 2.  
When the Laplace method was used, the accuracy was 68%. The accuracy of SobelX was similar to that of SobelY. Canny showed higher accuracy than the other methods and has been widely used in edge detection. Therefore, the Canny edge detection method is the best algorithm for detecting the edge of the target contour in a turbid area. In practical engineering applications, an algorithm that can identify the underwater bios trajectory of fisheries to enable easier localization and capture has also been investigated. As an optimization tool, the genetic algorithm has been widely used in various fields, but it has not been fully studied in terms of the trajectory prediction of moving targets. However, in a recent study, the concept of the dynamic traveler problem based on the genetic algorithm and Newton equation of motion was used to obtain excellent results in predicting the minimum distance traveled by a moving fishing boat in the future.