Improved Adaptive Finch Clustering Sonar Segmentation Algorithm Based on Data Distribution and Posterior Probability

This study proposes a side-scan sonar target detection technique for CPU or low-performance GPU to meet the requirement of underwater target detection. To rectify the gray distribution of the original side scan sonar data, enhance picture segmentation, and supply the data distribution probability for the clustering algorithm, the methodology uses a classic image processing technique that is GPU-friendly. The modified adaptive Finch clustering technique is used to segment the image and remove image voids after assessing the processed image attributes. The posterior information is then used to apply a classification label to each pixel. The characteristics of the connected region are analyzed in the data playback of the Tuandao experiment in accordance with the imaging principle of side-scan sonar and the original shape and size characteristics of the target. The predicted target results are combined with the AUV navigation information to obtain the predicted target longitude and latitude information, which is then sent to the AUV master control system to guide the next plan. The Jiaozhou Bay sea test results demonstrate that the traditional target detection algorithm put forth in this paper can be integrated into a low-performance GPU to detect targets and locate them. The detection accuracy and speed exhibit strong performance, and real-time autonomous sonar detection is made possible.