Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, TR109FE, UK.
Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, EX44QF, UK.
Commun Biol. 2024 Apr 3;7(1):354. doi: 10.1038/s42003-024-05979-z.
The invasive hornet Vespa velutina nigrithorax is a rapidly proliferating threat to pollinators in Europe and East Asia. To effectively limit its spread, colonies must be detected and destroyed early in the invasion curve, however the current reliance upon visual alerts by the public yields low accuracy. Advances in deep learning offer a potential solution to this, but the application of such technology remains challenging. Here we present VespAI, an automated system for the rapid detection of V. velutina. We leverage a hardware-assisted AI approach, combining a standardised monitoring station with deep YOLOv5s architecture and a ResNet backbone, trained on a bespoke end-to-end pipeline. This enables the system to detect hornets in real-time-achieving a mean precision-recall score of ≥0.99-and send associated image alerts via a compact remote processor. We demonstrate the successful operation of a prototype system in the field, and confirm its suitability for large-scale deployment in future use cases. As such, VespAI has the potential to transform the way that invasive hornets are managed, providing a robust early warning system to prevent ingressions into new regions.
入侵性胡蜂 Vespa velutina nigrithorax 是欧洲和东亚传粉媒介的快速增殖威胁。为了有效地限制其传播，必须在入侵曲线的早期检测和消灭巢穴，但目前依赖公众的视觉警报产生的准确率较低。深度学习的进步为此提供了潜在的解决方案，但这种技术的应用仍然具有挑战性。在这里，我们提出了 VespAI，这是一个用于快速检测 V. velutina 的自动化系统。我们利用硬件辅助的 AI 方法，将标准化监测站与深度 YOLOv5s 架构和 ResNet 骨干网相结合，在定制的端到端管道上进行训练。这使得系统能够实时检测胡蜂-实现平均精度-召回率≥0.99-并通过紧凑的远程处理器发送相关的图像警报。我们在现场展示了原型系统的成功运行，并确认其适合未来使用案例中的大规模部署。因此，VespAI 有可能改变入侵性胡蜂的管理方式，为防止新地区入侵提供强大的预警系统。
