Detection of Norway Spruce Trees (Picea Abies) Infested by Bark Beetle in UAV Images Using YOLOs Architectures

• Published in: IEEE access 2022, Vol.10, p.10384-10392
• Main Authors: Safonova, Anastasiia, Hamad, Yousif, Alekhina, Anna, Kaplun, Dmitry
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Autonomous aerial vehicles; Bark; bark beetle; Beetles; Computer architecture; Damage detection; Europe; Forestry; Monitoring; Norway spruce; object detection; Task analysis; Training; Trees; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Vegetation; you only look once (YOLO)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3144433

In recent years, massive outbreaks of the European spruce bark beetle ( Ips typographus , (L.)) have caused colossal harm to coniferous forests. The main solution for this problem is the timely prevention of the bark beetle spread, for which it is necessary to identify damaged trees in their early stages of infestation. Fortunately, high-resolution unmanned aerial vehicle (UAV) imagery together with modern detection models provide a high potential for addressing such issues. In this work, we evaluate and compare three You Only Look Once (YOLO) deep neural network architectures, namely YOLOv2, YOLOv3, and YOLOv4, in the task of detecting infested trees in UAV images. We built a new dataset for training and testing these models and used a pre-processing balance contrast enhancement technique (BCET) that improves the generalization capacity of the models. Our experiments show that YOLOv4 achieves particularly good results when applying the BCET pre-processing. The best test result when comparing YOLO models was obtained for YOLOv4 with the mean average precision up to 95%. As a result of applying artificial data augmentation, the improvement for models YOLOv2, YOLOv3, and YOLOv4 was 65.0%, 7.22%, and 3.19%, respectively.