Erratum to: Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition

Abstract

The underwater robot is one of the most important research tools in deep-sea exploration where the high pressure, extreme darkness, and radio attenuation prevent direct access. In particular, Autonomous Underwater Vehicles (AUVs) are the focus of much attention since they do not have tethered cables and can navigate freely. However, ideally, AUVs should be able to make independent decisions even with limited information using mounted sensors. That is, AUVs need powerful programming and low-power consumption computer systems which enable them to recognize their surroundings and cruise for long distances. Thus, modern computer development makes it possible for AUVs to be one of the most practical solutions for deep-sea exploration and investigations. As a next-generation AUV, we have been developing a Sampling-AUV that can dive into deep-sea regions and bring back samples of marine creatures in order to more fully understand the marine ecosystem. Our mission scenario calls for the Sampling-AUV to transmit deep-sea floor images to scientists on the research ship using acoustic communication. The scientists select the marine creatures to sample, and the AUV is tasked with retrieving them. The AUV then returns to the area where the interesting marine creatures have been observed, and collects and brings back samples. In order to realize this mission scenario, the sea-floor images need to be enhanced to assist the judgment of the scientists as the color red attenuates rapidly and the images become bluish while small differences in AUV altitude to the sea-floor also affect the brightness of the images due to light attenuation. Moreover, although underwater acoustic communication is slow and inaccurate, the AUV is required to select interesting images that include marine life. In this paper, we propose a deep-sea floor image enhancement method based on the Retinex theory and its performance was evaluated using deep-sea floor images taken by an AUV. The performance of the image enhancement was evaluated through crab recognition.