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首页> 外文会议>2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems >Detection and classification of stochastic features using a multi-Bayesian approach
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Detection and classification of stochastic features using a multi-Bayesian approach

机译：使用多贝叶斯方法对随机特征进行检测和分类

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摘要

This paper introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. This approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike conventional methods, these features' uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 25% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor.
机译：本文介绍了一种多贝叶斯框架，该框架用于在存在引起错误的噪声的环境中对特征进行检测和分类。这种方法在三个不同阶段利用了贝叶斯校正和分类。此处描述的纠正方案从数据源（无论是基于视觉还是其他）中提取有用但高度随机的特征，以帮助进行更高级别的分类。与常规方法不同，这些特征的不确定性具有特征，因此可以使用概率分布函数将测试数据正确地投射到特征空间中，该概率分布函数可以在由二次贝叶斯分类器创建的类决策边界上进行积分。所提出的方法是专门为道路裂缝检测和表征而制定的，这是潜在的应用之一。对于使用此技术评估的测试图像，准确地估计了地面真实性，并且与有效的贝叶斯校正一致，表明召回率比标准分类提高了25％。在道路裂缝中的应用证明了在实际领域中的成功检测和分类。当可以从多个传感器或从单个传感器顺序获得的数据中获得几个相关的观测值时，所提出的方法在表征嘈杂环境中的高概率特征时非常有效。

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