A blending method for forward-looking sonar mosaicing handling intra- and inter-frame artifacts

Abstract

Forward-looking sonar (FLS) has been gaining attractions in the realm of near-bottom, close-range underwater inspections, owing to its high-resolution and rapid framerate capabilities. Although automatic target recognition (ATR) algorithms have been tentatively employed for object detection tasks, the necessity for human oversight remains vital, particularly in sensitive areas. A comprehensive FLS mosaic encapsulating all relevant information is highly desired to aid experts in managing an extensive array of perception data. Yet, prior research has often presumed that FLS operates under an ideal system configuration, assuming optimal sonar imaging setups and the availability of precise positioning data. Without these assumptions, intra-frame and inter-frame artifacts would emerge, deteriorating the quality of the resultant mosaic by obscuring essential information. In this study, we propose an innovative blending method specifically for FLS mosaicing aiming preserve important information. We first develop a long–short time sliding window (LST-SW) to rectify the local statistics of raw sonar images. These statistics then serve to create a global variance map (GVM). This step helps to emphasize the useful information contained in images in the blending phase by classifying the informative and featureless pixels, thereby enhancing the quality of final mosaic. This approach is substantiated through data collected in real-world scenarios. The results show that our method can preserve more details in FLS mosaics for human inspection purposes in practice.