The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. Here we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached a high F1 score up to 0.874 in the infected bee detection and up to 0.727 in the detection of the Varroa Destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for this purpose. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies.

中文翻译：

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使用目标检测器技术可视化诊断蜜蜂中的Varroa破坏性寄生螨

Varroa破坏性螨是全世界最危险的蜜蜂（蜜蜂）寄生虫，必须定期监测蜂群，以控制其传播。在这里，我们提出了一种基于对象检测器的蜂群健康状态监测方法。这种方法具有在线测量和处理的潜力。在我们的实验中，我们将YOLO和SSD对象检测器与Deep SVDD异常检测器进行了比较。根据自定义数据集，该数据集具有600种不同情况下健康和受感染蜜蜂的地面真实图像，检测器在被感染蜜蜂检测中达到了最高F1分数，最高为0.874，而在Varroa Destructor螨的检测中达到了0.727。结果证明了这种方法的潜力，该方法将在以后基于实时计算机视觉的蜜蜂检测系统中使用。据我们所知，这项研究是第一个为此目的使用物体检测器的研究。我们希望这些物体检测器的性能将使我们能够检查蜜蜂群体的健康状况。