# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 1 Introduction  
Nowadays, underwater intelligent sensing technology is widely used in seabed resource exploration, fishery monitoring, underwater archaeology, underwater warfare, pipeline maintenance, and other fields. It also benefits the economy, military, culture, and other aspects. The future in this field is immeasurable, and the demand for large-scale and long-term monitoring of the internal water body is increasing. Due to the unique underwater operation environment, it has significant benefits but is also accompanied by some challenges. Turbidity is often encountered in underwater development as the required targets always exist in complex water environments, and water contains a variety of organic and inorganic suspended particles. Thus, the direction of light transmission is changed by the scattering or absorption of water and particles, which results in significant interference in the reflected light received by the imaging system, resulting in a significant reduction in the clarity of underwater images. Compared with turbid water, intuitively, the quality and visibility of clear water is good. For example, in a small clean pond, the target characteristics obtained via visual sensing are easily recognized due to the small amount of biological impurities and sediment in the water. Shallow water has the advantage of good light transmittance. However, target detection in turbid water is a significantly challenging task. Turbid water can be divided into shallow turbid water and two kinds of deep turbid water. Shallow turbid water, such as turbid fish farms, has a significant impact on the transmission of light information due to the high density of aquatic organisms and suspended matters, such as fishes and sediments, in the water. This leads to significant image distortion, such as blurred target features, severe distortion,and color changes, which pose a significant challenge regarding visual technology and target recognition. Deep turbid water, such as some deep water areas, is challenged by the same problems as shallow turbid water and has low light conditions, resulting in the instrument receiving limited effective target light information. Due to these factors, traditional target detection and recognition methods cannot meet instruments' technical requirements. Therefore, the investigation of object detection and recognition in turbid areas is necessary. Currently, research on underwater vision is mainly focusing on scenarios with good water conditions, such as experimental pools, lakes, inland rivers, etc. Due to the complexity of underwater environments, significant differences between various types of water exist. As large-scale engineering operations are often carried out at sea, most research methods do not contain adequate robustness to overcome the significant difficulties encountered in practical engineering applications. Focusing on the common applications scenes of turbid areas, this paper systematically collates the relevant research methods and the latest achievements, analyzes key technical problems, and summarizes future development directions. Since target detection in turbid water is more unique than that in clean water, this paper presents three perspectives: the first perspective is target feature extraction and detection based on deep learning methods, the second perspective is underwater image restoration and enhancement, and the last perspective is image processing based on the polarization imaging approach. The contents of this paper are summarized as follows: (1) the application of ConvNet and a typical network, such as Faster RCNN [1] and YOLOv3 [2], and a comparison of the Canny edge detection algorithm [3] and a track prediction algorithm combined with practical engineering are introduced, and the disadvantages of deep learning methods and their corresponding solutions are analyzed. (2) Some methods, including the migrating defogging algorithm and addition of the haze reduction module, for restoring and enhancing underwater images are summarized.