Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world. For the development of effective forest policies and management plans, the early detection of infested trees is essential. Despite the visual symptoms of bark beetle infestation, this task remains challenging, considering overlapping tree crowns and non-homogeneity in crown foliage discolouration. In this work, a deep learning based method is proposed to effectively classify different stages of bark beetle attacks at the individual tree level. The proposed method uses RetinaNet architecture (exploiting a robust feature extraction backbone pre-trained for tree crown detection) to train a shallow subnetwork for classifying the different attack stages of images captured by unmanned aerial vehicles (UAVs). Moreover, various data augmentation strategies are examined to address the class imbalance problem, and consequently, the affine transformation is selected to be the most effective one for this purpose. Experimental evaluations demonstrate the effectiveness of the proposed method by achieving an average accuracy of 98.95%, considerably outperforming the baseline method by approximately 10%.
翻译: ⁇ 虫的暴发可以极大地影响世界各地的森林生态系统和服务。为了制定有效的森林政策和管理计划,必须及早发现受侵扰的树木。尽管树皮甲虫的侵扰有目共睹的症状,但考虑到树冠重叠和树叶色脱色的树冠不均匀性,这项任务仍然具有挑战性。在这项工作中,提议了一种基于深层次学习的方法,以有效地分类个别树层的树皮虫袭击的不同阶段。拟议方法使用RetinaNet结构(开发经过事先培训的坚固的特征提取骨干)来训练一个浅小网络,对无人驾驶航空飞行器(UAVAs)所捕捉到的图像的不同攻击阶段进行分类。此外,对各种数据增强战略进行了研究,以解决阶级不平衡问题,因此,为了达到这一目的,草根转变被选定为最有效的方法。实验性评估通过实现98.95%的平均准确率,表明拟议方法的有效性,大大超过基准方法的10%。