# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 6 Conclusions  
In this manuscript, target detection methods in turbid waters were divided into two areas. One focused on alleviating the problems in image degradation caused by the special conditions of turbid water. The establishment of a model, application of image restoration algorithms, or the use of polarized images can pertinently process images. The other area focused on some approaches regarding underwater target detection and recognition that consider the features of underwater images, target feature extraction, and actual engineering requirements. Appropriate deep learning and ML networks can be used to diversify this research, and it can also be flexibly used in future engineering.  
This paper analyzed the target detection and recognition methods used in turbid waters and classified them into three categories: target detection based on depth learning approaches, underwater image restoration and enhancement methods, and underwater image processing methods based on polarization imaging technology and scattering. It also introduced other methods, such as the use of sonar to identify pipes and the use of electronic fields for target detection, and summarized relevant datasets. However, some problems still need to be analyzed. For example, there is no uniform evaluation system for image processing in turbid waters, the robustness of target detection algorithms is inadequate, and the target detection distance of polarization imaging technology in turbid waters is lacking. Comprehensive target detection and recognition technology in turbid water can be effectively integrated with an information management system to meet the requirements of clear imaging in turbid water, and to solve the problems of biological observation, pipeline inspection, exploration detection, and evaluation in high-turbidity water. This technique can be combined with advanced information processing technologies, such as large data, to analyze and evaluate underwater engineering structures and to prevent catastrophic accidents. Thus, it reduces adverse consequences and economic losses caused by structural damage, provides more accurate statistical data and breeding and fishing methods for fishing grounds, offers efficient tools for underwater ecological environment monitoring, and devises innovative technologies for underwater exploration.  
In the future, with the development of deep learning and image approaches, more algorithms that are suitable for turbid areas need to be developed; an underwater light model library covering a variety of water environments needs to be established; and a more unified, objective, and comprehensive underwater image evaluation system needs to be proposed. Relevant breakthroughs in this technology will certainly drive the progress and development of related fields and help to realize industrialization and automation. This will result in broad application prospects and have significant social benefits.  
Conceptualization, X.Y. and C.Y.; validation, X.Y., L.G. and X.Z.; writing--original draft preparation, L.G.; writing--review and editing, X.Y., L.G., C.L., X.Z. and C.Y.; validation, L.G.; visualization, L.G.; supervision, X.Y. and L.G.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research leading to the presented results has been undertaken within the Chinese Shandong Provincial Key Research and Development Plan, under Grant No. 2019 GHZ011 and No. 2021 CXGC010702.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The research leading to the presented results has been undertaken within the Chinese Shandong Provincial Key Research and Development Plan, under Grant No. 2019 GHZ011 and No. 2021 CXGC010702.  
**Conflicts of Interest:** The authors declare no conflict of interest.