Underwater object detection and tracking based on multi-beam sonar image processing

A new framework capable of analyzing multi-beam sonar images for detecting and tracking underwater object using a BlueView (BV) Sonar is presented in this paper. This framework is applied to the design of an obstacle avoidance system for Unmanned Measurement Boat. The real-time sonar data flow collected by multi-beam sonar is expressed as an image and pre-processed by the system. According to the characteristics of sonar images, an improved Otsu method has been carried out to detect the object combining with the contour detection algorithm, with which the foreground object can be separated from background successfully. Then the object is tracked by a particle filter tracking method based on multi-feature adaptive fusion. Results obtained on real sonar data show that the proposed framework can detect and track the object accurately and robustly.