23 May 2011 Automation for underwater mine recognition: current trends and future strategy
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Abstract
The purpose of this paper is to define the vision and future strategy for advancing the use of automation in underwater mine recognition. The technical portion of this strategy is founded on the principle of adapting the automation in situ based on a highly variable environment / context and the occasional availability of the human operator. To frame this strategy, a survey of past and current algorithm development for underwater mine recognition is presented and includes a detailed description on adaptive algorithms. This discussion is motivated by illustrating the extreme variability in the underwater environment and that performance estimation techniques are now emerging that are capable of quantifying these variations in situ. It is the in situ linkage of performance estimation with adaptive recognition that forms one of the key technological enablers of this future strategy. The non-technical portion of this strategy is centered on enabling an effective human-machine team. Enabling this teaming relationship involves both gaining trust and establishing an overall support system that is amenable to such human-machine interactions. Aspects of trust include both individual trust and institutional trust, and a path for gaining both is discussed. Overall aspects of the support system are highlighted and include standards for data and interoperability, network-centric software architectures, and issues in proliferating knowledge that is learned in situ by multiple distributed algorithms. This paper concludes with an articulation of several important and timely research questions concerning automation for underwater mine recognition.
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Jason Stack, Jason Stack, } "Automation for underwater mine recognition: current trends and future strategy", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170K (23 May 2011); doi: 10.1117/12.884475; https://doi.org/10.1117/12.884475
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