Detection of underwater objects based on machine learning

• Published in: Journal of the Japan Society of Naval Architects and Ocean Engineers Vol. 18; pp. 2104 - 2109
• Main Authors: Tan, Yasuhiro, Tan, Joo Kooi, Kim, Hyoungseop, Ishikawa, Seiji
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Tokyo The Society of Instrument and Control Engineers - SICE 01.01.2013; The Japan Society of Naval Architects and Ocean Engineers; Japan Science and Technology Agency
• Subjects: Accuracy; Feature extraction; Global Positioning System; Haar-like features; Image detection; Image edge detection; Machine learning; Marine; Naval; Naval engineering; Sea beds; Side-scan sonar; Sonar; Sonar detection; Sonar navigation; Underwater; underwater objects
• ISSN: 1880-3717; 1881-1760
• DOI: 10.2534/jjasnaoe.18.115

Side-scan and forward-looking sonars are some of the most widely used imaging systems for obtaining large scale images of the seafloor, and their use continues to expand rapidly with their increased deployment on autonomous underwater vehicles. However, it is difficult to extract quantitative information from the images generated from these processes, particularly for the detection and extraction of information on the objects within these images. We propose in this paper an algorithm for automatic detection of underwater objects in side-scan images based on machine learning employing adaptive boosting. Experimental results show that the method produces consistent maps of the seafloor.