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Characterization of Entomological Micro Traces Images with Deep Neural Networks

机译:深神经网络的昆虫学微迹图像的特征

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Micro traces analysis is a new and growing area of research within forensic science. One of the possible applications of this knowledge is to assist in determining the area of origin of criminal elements. Micro traces of insects are found in many crime contexts such as inside narcotics, money bags or even in remains. Forensic entomology has been increasingly used to characterize insects found in these contexts. Insect characterization is a slow and complex process, mainly because specimens are usually found in pieces. To simplify and accelerate this analysis, this paper presents the use of a deep neural network to characterize insects or their parts up to the taxonomic level of the family from specimen images. The developed system reached a classification accuracy of 75.0% among five evaluated entomological families: Buprestidae, Calliphoridae, Formicidae, Muscidae and Pentatomidae.
机译:微迹线分析是法医学中的一个新的和不断增长的研究领域。本知识的可能应用之一是有助于确定犯罪分子的原产地。在许多犯罪环境中发现了微量痕迹,例如毒品,金钱袋甚至遗骸。法医昆虫学越来越多地用于表征在这些背景下发现的昆虫。昆虫表征是一种缓慢而复杂的过程,主要是因为样品通常被发现。为了简化和加速这一分析,本文介绍了深度神经网络,将昆虫或其部件表征为来自标本图像的家族的分类水平。发达的系统在五个评估的昆虫学家庭中达到了75.0%的分类准确性:Buprestidae,Calliphoridae,Formicidae,Muscidae和戊酰胺。

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