Active Classification: Theory and Application to Underwater Inspection
Active Classification: Theory and Application to Underwater Inspection
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主动分类：水下检测的理论与应用
DOI：
10.1007/978-3-319-29363-9_6
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
G. Sukhatme
中科院分区：
文献类型：
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作者：
Geoffrey A. Hollinger;U. Mitra;G. Sukhatme
我们讨论了自动驾驶车辆必须基于多个视图对物体进行分类的问题。我们专注于主动分类设置，其中车辆控制选择哪些视图以最佳地执行分类。该问题被表述为贝叶斯主动学习的扩展，我们展示了与该领域最新理论保证的联系。我们正式分析了在获得新信息时采取自适应行动的好处。分析得出了一种基于信息论代价的概率算法，用于确定观测的最佳视图。我们通过两种方法验证了我们的方法，这两种方法都与水下检测有关：合成深度图中的3D多面体识别和成像声纳的船体检测。这些任务包括主动分类问题的规划和识别两个方面。结果表明，与被动方法相比，主动规划信息视图可以减少多达80%的必要视图数量。
We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80 % when compared to passive methods.