Improved SSS small target detection method based on kernel regression and patch-image model

Abstract With the rapid advancement of unmanned technology, autonomous underwater vehicles equipped with side-scan sonar are playing an increasingly vital role in the realm of underwater exploration. The detection of underwater small targets such as mine-like objects, unexploded ordnance, and rod-shaped items is a focal point of current sonar technology research, playing a crucial role in military struggle. However, existing methods overly rely on the prior shadow information and are prone to missing numerous small targets. To address this, we propose a weighted SSS patch-image model. The method is an improved method for small target detection in SSS images based on iterative steering kernel regression and patch-image model. The method enables effective detection of small targets without considering shadow information. Firstly, kernel regression is employed using steering kernels to denoise the images while preserving edge information. Subsequently, a small target detection model is constructed using the patch-image model by considering the reconstructed SSS image, the target image, the background image, and the noise image. According to the experimental results, the improved small target detection method combining the two aforementioned algorithms demonstrates high accuracy and reliability in detecting underwater small targets on SSS images. Comparative experiments further reveal that this approach overcomes the limitations of traditional methods that rely heavily on target shadow information, establishing it as an efficient and robust solution for underwater small target detection in SSS images.