Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/153668
Title: AUTOMATIC DETECTION AND RECOGNITION OF MINE-LIKE OBJECTS IN SIDE-SCAN SONAR IMAGES
Authors: ANUPAM MAZUMDAR
Keywords: Side-scan Sonar
Sea Mine
Wavelet
Shadow
Embedded Processor
Issue Date: 2009
Citation: ANUPAM MAZUMDAR (2009). AUTOMATIC DETECTION AND RECOGNITION OF MINE-LIKE OBJECTS IN SIDE-SCAN SONAR IMAGES. ScholarBank@NUS Repository.
Abstract: In this report, a study is presented for automatic detection and recognition of sea mines in sound navigation and ranging (SONAR) imagery. The processing steps include (i) pre-processing, (ii) mine-like object detection, and (iii) recognition through shadow verification. A new adaptive wavelet-based algorithm is developed for automatic detection and recognition of mine-like objects in sidescan sonar images. The detection algorithm is particularly suitable for implementation on embedded platforms. Another significant advantage of the algorithm is that it is insensitive to changes in the seafloor or background clutter. The algorithm was tested on a set of high-frequency high-resolution side-scan sonar images containing mines as well as seafloor clutter. The obtained results were found to be successful in 90% of the cases.
URI: https://scholarbank.nus.edu.sg/handle/10635/153668
Appears in Collections:Master's Theses (Restricted)

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