Improving Automatic Target Recognition with Forward Looking Sonar Mosaics

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• 作者：F. Ferreira^* ; ^{fausto.ferreira@ieiit.cnr.it" class="auth_mail" title="E-mail the corresponding author} ; V. Djapic^** ; ^{vladimir.djapic@navy.mil" class="auth_mail" title="E-mail the corresponding author} ; M. Micheli^*** ; ^{micheli@cmre.nato.int" class="auth_mail" title="E-mail the corresponding author} ; M. Caccia^**** ; ^{max@ge.issia.cnr.it" class="auth_mail" title="E-mail the corresponding author}
• 关键词：mosaicking ; forward looking sonar ; automatic target detection ; acoustic image processing
• 刊名：IFAC-PapersOnLine
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：47
• 期：3
• 页码：3382-3387
• 全文大小：624 K

文摘

Automatic Target Recognition (ATR) is a key element needed to make Mine Countermeasure missions using robots entirely autonomous. While there has been much progress in applying ATR algorithms on high-resolution Synthetic Aperture Sonar (SAS) and sidescan sonar data, performing ATR with a low cost Forward Looking Sonar (FLS) is much more challenging. An algorithm for the detection of underwater man-made objects in FLS previously developed can work in real-time although it suffers considerably from typical noise in sonar images and false alarms. The work presented here shows that ATR algorithms can be exercised on sonar mosaics built also in real-time instead of raw data coming from the FLS. The use of mosaics can help the detection of the targets by reducing some noise (including harmonics from other acoustic devices mounted on the robot) and giving a better contrast to the images to be processed. Moreover, mosaic images can be useful for post-processing and data analysis. The mosaicking algorithm also runs in real-time to maintain the performance of the system and to be useful in real missions. It was tested both on data previously collected and in real experiments with different set-ups and with different sonars. The wide range of results obtained with different surface vehicles and in different situations demonstrate the usefulness of the method.