Swarm intelligence based segmentation for buried object scanning SONAR images

In the present work, Particle swarm optimisation (PSO) based Tsallis entropy method is employed to segment the buried object SONAR images. This SONAR detects the objects present beneath the seabed in ocean. Objects may be pipelines, and unexploded ordinances buried beneath the seabed. Computer vision for object detection is required when SONAR is equipped in autonomous underwater vehicle. The vehicle acquires volumes of data to be analysed manually which is time consuming and expensive. In Tsallis entropy segmentation method, PSO based optimisation technique is employed to select the appropriate bilevel thresholds for every image. For the considered image `mild steel and concrete´ threshold value is 129 and the corresponding accuracy is 99.14 %. The threshold value for the image `stone´ is 132 and the accuracy is 97.5 % for `q´ value 0.2.