Development of an Image Processing Module for Autonomous Underwater Vehicles through Integration of Visual Recognition with Stereoscopic Image Reconstruction

This study investigated the development of visual recognition and stereoscopic imaging technology, applying them to the construction of an image processing system for autonomous underwater vehicles (AUVs). For the proposed visual recognition technology, a Hough transform was combined with an optical flow algorithm to detect the linear features and movement speeds of dynamic images; the proposed stereoscopic imaging technique employed a Harris corner detector to estimate the distance of the target. A physical AUV was constructed with a wide-angle lens camera and a binocular vision device mounted on the bow to provide image input. Subsequently, a simulation environment was established in Simscape Multibody and used to control the post-driver system of the stern, which contained horizontal and vertical rudder planes as well as the propeller. In static testing at National Cheng Kung University, physical targets were placed in a stability water tank; the study compared the analysis results obtained from various brightness and turbidity conditions in out-of-water and underwater environments. Finally, the dynamic testing results were combined with a fuzzy controller to output the real-time responses of the vehicle regarding the angles, rates of the rudder planes, and the propeller revolution speeds at various distances.