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Characterization of Entomological Micro Traces Images with Deep Neural Networks

机译:深度神经网络对昆虫微迹图像的表征

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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:微量痕迹分析是法医学领域研究的一个新兴领域。该知识的可能应用之一是协助确定犯罪分子的来源地区。在许多犯罪情况下,例如在麻醉品,钱袋内甚至在遗骸中,都发现了微量的昆虫。法医昆虫学已越来越多地用于表征在这些情况下发现的昆虫。昆虫鉴定是一个缓慢而复杂的过程,主要是因为标本通常成碎片地发现。为了简化和加速这一分析,本文提出了使用深度神经网络从标本图像中表征昆虫或昆虫的部分,直至达到该家族的分类学水平。所开发的系统在五个评估的昆虫科中达到了75.0%的分类准确度:B科,Calliphoridae,Formicidae,Muscidae和Pentatomidae。

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