Evaluation criterion of underwater object clustering segmentation with pulse‐coupled neural network

Abstract

The success of clustering algorithms in object segmentation depends on the quality of the evaluation criterion. However, sonar images are seriously affected by noise. Most of the existing evaluation criteria such as the Davies Bouldin (DB) criterion only considers their pixel value, and sonar image information extraction is not sufficient. As a result, they fail to achieve good underwater object segmentation results. To overcome this problem, this study proposes an improved DB criterion with pulse -coupled neural network (PCNN), which is called the DB-PCNN. In the calculation process of DB-PCNN, the role of internal activity items in PCNN is considered, which can make better use of pixel information in adjacent space on the sonar image. The experimental results show that DB-PCNN can further improve the accuracy of underwater object segmentation and has certain adaptability to different optimisation frameworks.