# 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  
In recent years, deep learning technology has been widely used in underwater image defogging and target identification. Methods based on deep learning investigate image sets by training the neural network and seek to establish a logical relationship to improve the image clarity or extract target features for intelligent recognition. In turbid waters, such as typical fish farms, monitoring of fish in real-time is required. Therefore, accurate identification of targets and prediction of motion trajectories in turbid waters is important. Due to the typical problems, such as low contrast, a lack of inherent features, blurring, and distortion, caused by the turbidity of water or the presence of suspended particles in water in aquaculture farms, target detection of fish is difficult. This section discusses the applicability of deep learning networks for target recognition in turbid waters and methods to improve the deep learning defects. Traditional target detection methods manually extract the features of target areas, which is time consuming and has poor robustness. With the appearance of the deep learning convolution neural network, the target detection algorithm entered a new stage. Existing target detection algorithms are mainly divided into two-stage algorithms and one-stage algorithms. Two-stage algorithms mainly include the RCNN series algorithms (RCNN [9], Fast RCNN [10], Faster RCNN [1]). These algorithms first generate regional proposals, and then perform classification and regression tasks on the regional proposals. Thus, detection is improved, but the processing time is increased accordingly. Such algorithms are more suitable for the detection of static underwater objects, such as seafloor rocks, corals, etc. Single-stage algorithms mainly include the SSD [11] algorithm and YOLO series algorithms (YOLO [12], YOLOv2 [13], YOLOv3 [2]). These algorithms improve the detection speed and maintain the detection effect as much as possible and use the direct regression method to forecast the category and location of targets. Therefore, such an algorithm is suitable for when the target detection required is frequent due to frequent aquatic activities, such as those of fish. Effective target feature detectors and classifiers provide deep learning methods with an advantage in turbid water environments, which is why it is important for computers  
Figure 3: Comparison of images before and after underwater image processing. (**a**) Before analysis of the image. (**b**) After analysis of the image. to adapt the fuzzy characteristic of turbid water and precisely identify target features. [PERSON] and [PERSON] [14] proposed two types of classifiers: one is a two-classification convolution neural network for distinguishing between the target and background while the other is a multi-classification convolution neural network for predicting the background or one type of target, such as shoes and ropes. They trained a convolution neural network (CNN) to classify 64 \(\times\) 64 inputs. The images were classified, and the classifier was used as the target feature detector. The accuracy rate of the 2-class convolution neural network was 93.9%, and the target detection accuracy rate of the multi-class convolution neural network was 90.1%, which is higher than existing multi-class detectors (88%). [PERSON] et al. [15] focused on the selection and improvement of the basic network architecture in Faster RCNN. They preprocessed the acquired images and tested the basic network performance under different network architectures in Faster RCNN to select the best basic network for image training in turbid media. The experimental results showed that the accuracy of MobileNetV2 [16] on the basic network was 87.52%, which is better than other architectures. A comparison of the experimental results is shown in Table 1.  
To extract features from turbid areas with significant interference, [PERSON] et al.