Alon Zvirin, Amitzur Shapira, Emma Attal, Tamar Gozlan, Arthur Soussan, Dafna De La Vega, Yehudit Harush, Ron Kimmel
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引用次数: 0
Abstract
The detection of cannabis and cannabis-related products is a critical task for forensic laboratories and law enforcement agencies, given their harmful effects. Forensic laboratories analyze large quantities of plant material annually to identify genuine cannabis and its illicit substitutes. Ensuring accurate identification is essential for supporting judicial proceedings and combating drug-related crimes. The naked eye alone cannot distinguish between genuine cannabis and non-cannabis plant material that has been sprayed with synthetic cannabinoids, especially after distribution into the market. Reliable forensic identification typically requires two colorimetric tests (Duquenois-Levine and Fast Blue BB), as well as a drug laboratory expert test for affirmation or negation of cannabis hair (non-glandular trichomes), making the process time-consuming and resource-intensive. Here, we propose a novel deep learning-based computer vision method for identifying non-glandular trichome hairs in cannabis. A dataset of several thousand annotated microscope images was collected, including genuine cannabis and non-cannabis plant material apparently sprayed with synthetic cannabinoids. Ground-truth labels were established using three forensic tests, two chemical assays, and expert microscopic analysis, ensuring reliable classification. The proposed method demonstrated an accuracy exceeding 97% in distinguishing cannabis from non-cannabis plant material. These results suggest that deep learning can reliably identify non-glandular trichome hairs in cannabis based on microscopic trichome features, potentially reducing reliance on costly and time-consuming expert microscopic analysis. This framework provides forensic departments and law enforcement agencies with an efficient and accurate tool for identifying non-glandular trichome hairs in cannabis, supporting efforts to combat illicit drug trafficking.
鉴于大麻和大麻相关产品的有害影响,检测大麻和大麻相关产品是法医实验室和执法机构的一项关键任务。法医实验室每年分析大量的植物材料,以鉴定真正的大麻及其非法替代品。确保准确的鉴定对支持司法程序和打击与毒品有关的犯罪至关重要。仅凭肉眼无法区分喷洒了合成大麻素的真大麻和非大麻植物材料,特别是在进入市场之后。可靠的法医鉴定通常需要两次比色测试(Duquenois-Levine和Fast Blue BB),以及药物实验室专家测试,以确定或否认大麻毛发(非腺毛),这使得该过程既耗时又耗费资源。在这里,我们提出了一种新的基于深度学习的计算机视觉方法来识别大麻中的非腺毛。收集了数千张带注释的显微镜图像的数据集,包括真正的大麻和显然喷洒了合成大麻素的非大麻植物材料。通过三次法医测试、两次化学分析和专家显微镜分析,建立了基础真相标签,确保了可靠的分类。所提出的方法在区分大麻和非大麻植物材料方面的准确率超过97%。这些结果表明,深度学习可以根据微观毛状特征可靠地识别大麻中的非腺状毛状毛,从而有可能减少对昂贵且耗时的专家微观分析的依赖。该框架为法医部门和执法机构提供了一种有效和准确的工具,用于识别大麻中的非腺毛,支持打击非法贩毒的努力。