Real-Time Sonar Image Simulation for Adaptive Template Matching in Automatic Target Recognition

Autonomous underwater vehicles (AUVs) equipped with side-looking sonars have become vital tools for seafloor exploration due to the combination of high image resolution and high area coverage rates. To reach their full operational performance AUVs also need onboard perception, including recognition of relevant objects. We combine adaptive template matching and real-time image simulation for automatic target recognition in synthetic aperture sonar images. We hypothesize that dynamic, rapid and fine-tuned search of object types and configurations should improve classification results and real-time responses. Analyses of experimental data with cylindrical objects outside of Horten, Norway, recorded by the Kongsberg Maritime HISAS1030 sonar, strengthened the hypothesis. Our setup outperformed a well-configured, static template database at false positive rates (FPR) above 10%-20%, with an area under curve improvement of one to two percent, depending on the correlation methods used. The system is implemented on a graphics processing unit using OpenGL and OpenCL, a computer graphics and general-purpose programming library, respectively. This facilitates a faster and more flexible classification process. We describe the implementation and provide a supplementary Python script to showcase the notation and implementation in practice.