Optimizing Postprocessing of Range-Gated Viewing Data for Maritime Search and Rescue Operations at Night and in Bad Weather Conditions

Abstract

At night and bad weather conditions the detection of persons and objects floating in the sea represents a major challenge for search and rescue operations (SAR). If conventional searchlights are used, backscattering from rain, fog and snow decreases the detection range. Therefore, a compact and inexpensive range-gated viewing system was developed, which significantly reduces atmospheric backscattering. The instrument is designed for detection ranges of several hundred meters. In this study, different image processing techniques were analyzed in terms of improved object detectability for a human observer and for a machine learning based object detector, based on a real-world image dataset. On the one hand noise of the camera is reduced by performing a non-uniformity correction (NUC) and the other hand the dynamic range of the images is adjusted and dark objects are accentuated by equalizing (EQ). The aim of this field study with the subsequent post processing steps is to achieve with low computing power an improvement in visibility for both a human observer and a machine learning based object detector, which is based on a real-world image dataset. Results show, that processing requirements are different for both cases, mainly caused by the human eye perception, which an automated detector does not rely on and therefore the performance of the object detector before the equalizing step is slightly better. However, the NUC improves the image quality in any case.