Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics
By Daniel Queirós da Silva, Filipe Baptista Neves dos Santos, Armando Jorge Miranda de Sousa, Vítor Manuel de Jesus Filipe. 2021
38 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Ecology, Environmental Engineering, Mechanics of Materials, Robotics & Control
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Citations in the corpus
- Instance Segmentation for Autonomous Log Grasping in Forestry Operations (2022, background): “[25] published a dataset of standing trees for bounding boxes detection and trained a YOLOv3 [26] network on it.”