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首页> 外文期刊>International journal of image and data fusion >Feature-based detection using Bayesian data fusion
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Feature-based detection using Bayesian data fusion

机译：使用贝叶斯数据融合的基于特征的检测

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Current cocaine detection techniques used at borders have their challenges, which include cost of training specialised operators, the high chance of operator error and the dangers involved in exposure of both operators and container contents to radioactive material. This paper describes a technique which utilises the benefits of data fusion to develop a non-invasive system which relies less on the expertise of the operator, whilst improving false positive rates. To improve the capabilities of the cocaine-detecting fibre-optic sensor, the raw data was pre-processed and features were identified and extracted. The output of each feature is a decision on the classification and the conditional probability that it belongs to the chosen class based on the observed data, which serve as input into a Bayesian data fusion module and outputs the probability that a sample belongs to a class based on the observed features and makes a decision based on the class with the higher probability. The results show that the Bayesian fusion module greatly improves the detection rates of individual feature.
机译：当前在边界使用的可卡因检测技术面临挑战，其中包括培训专业操作员的成本，操作员失误的高可能性以及操作员和容器内容物均暴露于放射性物质中所涉及的危险。本文介绍了一种技术，该技术利用数据融合的优势开发了一种非侵入性系统，该系统较少依赖操作员的专业知识，同时提高了误报率。为了提高可卡因检测光纤传感器的功能，对原始数据进行了预处理，并对特征进行了识别和提取。每个特征的输出是根据观测数据对它属于所选类别的分类和条件概率的决定，这些输入用作贝叶斯数据融合模块的输入，并输出样本属于基于类别的概率对观察到的特征进行判断，并根据可能性较高的类别做出决策。结果表明，贝叶斯融合模块极大地提高了单个特征的检测率。

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