Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics

• Type: Article in International Scientific Journal
• Date: 2022

Authors

da Silva, DQ (Author) Other; Filipe Neves Santos (Author) FEUP; Filipe, V (Author) Other; Armando Jorge Sousa (Author) FEUP; Oliveira, PM (Author) Other

Journal

Title: Robotics

Vol. 11

Final page: 136

Publisher: MDPI

Indexing

ISI Web of Knowledge - 4 Citations

Other information

Authenticus ID: P-00X-G2B

DOI: 10.3390/robotics11060136

Abstract (EN): Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23