基于摄像头的飞虫自动监测(Camfi)。1 .场和计算方法

Jesse Rudolf Amenuvegbe Wallace, Therese Maria Joanna Reber, David Dreyer, Brendan Beaton, Jochen Zeil, Eric Warrant
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引用次数: 1

摘要

测量飞虫活动和数量的能力对生态学家、自然资源保护主义者和农学家都很重要。然而,现有的方法很费力,产生的数据时间分辨率很低(如诱捕和直接观测),或者价格昂贵,技术复杂,需要车辆进入现场(如雷达和激光雷达昆虫学)。我们提出了一种名为“Camfi”的方法,利用野生动物摄像机获取的图像和视频,对低空飞行昆虫进行长期无创种群监测和高通量行为观察,该方法价格低廉,操作简单。为了方便非常大的监测程序,我们开发并实现了一个工具,用于自动检测和注释在静止图像或视频剪辑中的飞虫目标基于流行的Mask R-CNN框架。利用迁移学习的优势,这个工具可以在几个小时内被训练来检测和注释昆虫。我们的方法将被证明对正在进行的了解昆虫种群减少的行为和生态的努力是无价的,也可以应用于农学。该方法特别适用于偏远地区低空飞行昆虫的研究,适用于非常大规模的监测项目,或预算相对较低的项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Camera-based automated monitoring of flying insects (Camfi). I. Field and computational methods
The ability to measure flying insect activity and abundance is important for ecologists, conservationists and agronomists alike. However, existing methods are laborious and produce data with low temporal resolution (e.g. trapping and direct observation), or are expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar entomology). We propose a method called “Camfi” for long-term non-invasive population monitoring and high-throughput behavioural observation of low-flying insects using images and videos obtained from wildlife cameras, which are inexpensive and simple to operate. To facilitate very large monitoring programs, we have developed and implemented a tool for automatic detection and annotation of flying insect targets in still images or video clips based on the popular Mask R-CNN framework. This tool can be trained to detect and annotate insects in a few hours, taking advantage of transfer learning. Our method will prove invaluable for ongoing efforts to understand the behaviour and ecology of declining insect populations and could also be applied to agronomy. The method is particularly suited to studies of low-flying insects in remote areas, and is suitable for very large-scale monitoring programs, or programs with relatively low budgets.
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