da Silva Daniel Queirós, Dos Santos Filipe Neves, Sousa Armando Jorge, Filipe Vítor
INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal.
School of Science and Technology, University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal.
J Imaging. 2021 Sep 3;7(9):176. doi: 10.3390/jimaging7090176.
Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.
由于森林野火的频繁发生，移动机器人在森林中的应用目前是一个极其重要的课题。因此，需要对森林资源清查和生物量进行现场管理。为了解决这个问题，这项工作提出了一项利用基于深度学习的目标检测方法在可见光和热图像中对森林树干进行地面检测的研究。为此，构建了一个由2895张图像组成的林业数据集并公开发布。使用该数据集，训练并基准测试了五个模型以检测树干。所选模型为SSD MobileNetV2、SSD Inception-v2、SSD ResNet50、SSDLite MobileDet和YOLOv4 Tiny。取得了有前景的结果；例如，YOLOv4 Tiny是表现最佳的模型，实现了最高的平均精度（AP，90%）和F1分数（89%）。还在CPU和GPU上对这些模型的推理时间进行了评估。结果表明，YOLOv4 Tiny是在GPU上运行速度最快的检测器（8毫秒）。这项工作将推动更智能林业机器人视觉感知系统的发展。
