# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Underwater Image Processing Based on Polarization Imaging and Scattering
The suppression of background backscattering interference also combines polarization imaging technology with image processing technology, which has greater engineering application value. [PERSON] [50] proposed a target detection algorithm based on guided filtering combined with ViBe and a secondary target detection algorithm based on polarization difference image and intensity image fusion combining image processing and polarization imaging technology. To eliminate the influence of the backscattering of scattered particles in turbid media, enrich the detailed information of the detection target, and improve the effectiveness and practicability of the target detection algorithm, the author established a database containing the light polarization characteristics of different turbid media and typical targets to provide prior knowledge for information analysis and target detection of polarization imaging in certain circumstances. The former has certain advantages for target detection in turbid liquids but has certain requirements regarding the turbidity range of the media. The latter has a greater experimental effect, improving the image quality and significantly suppressing the interference that occurs due to background backscattering. [PERSON] et al. [51] thought that the prediction of the correct target return value using ML would also produce a better backscattering interference suppression effect. This method is suitable for 3-D underwater imaging of signals reflected from targets using lidar in turbid waters. When the receiver encounters backscattering of scattered particles from turbid media, it produces false peaks (both from scattering reflections and from scatter-introduced shot noise), which makes it difficult to determine which peaks correspond to the object. Underwater lidar systems have been used in turbid waters and an ML model has been used to detect target signals. The experimental results showed that the accuracy of the ML classifier was significantly better than that of the baseline detector under high turbidity conditions, which indicates that this method could improve the performance of underwater lidar systems, especially for systems with a realized frequency domain and other scattering suppression techniques. The classifier worked well under low turbidity conditions, thus having good application prospects for high turbidity conditions. However, [PERSON] et al. [52] identified the limitations of previous polarization-based underwater image restoration methods and proposed a method for estimating the spatial distribution of the degree of polarization (DOP) and backscattering radiation based on extrapolation fitting. Past limitations include the assumption that DOP of backscattering and the irradiance of backscattering at infinite distances are constant. They believed that the assumption that the identification of the inherent parameters of underwater imaging conditions, including the degree of polarization of backscattering and the backscattering intensity at the various positions of an image, as spatial constants is inappropriate in many cases for polarized imaging in turbid mediums. For example, in inhomogeneous light fields, the intrinsic parameters of underwater imaging conditions may vary with the shooting position, and if these parameters are considered as constants, in some areas of the scene, the image quality may deteriorate. The experiment results of the new estimation method showed that the quality of underwater images can be significantly improved under the conditions of a non-uniform light field, and the quality of the restored images at various locations can be maintained at a high level. Regarding the significant light scattering effect evident in mixed water, a new method for target detection by backscattering was proposed. [PERSON] et al. [53] proposed a method for target detection and geometric contour analysis based on backscattering asymmetry.