Tree Trunks Cross-Platform Detection Using Deep Learning Strategies for Forestry Operations

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

da Silva, DQ (Autor) Outra; Filipe Neves Santos (Autor) FEUP; Filipe, V (Autor) Outra; Armando Jorge Sousa (Autor) FEUP

Ata de Conferência Internacional

Título: Lecture Notes in Networks and Systems

Páginas: 40-52

5th Iberian Robotics Conference, ROBOT 2022

23 November 2022 through 25 November 2022

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-FVK

DOI: 10.1007/978-3-031-21065-5_4

Abstract (EN): To tackle wildfires and improve forest biomass management, cost effective and reliable mowing and pruning robots are required. However, the development of visual perception systems for forestry robotics needs to be researched and explored to achieve safe solutions. This paper presents two main contributions: an annotated dataset and a benchmark between edge-computing hardware and deep learning models. The dataset is composed by nearly 5,400 annotated images. This dataset enabled to train nine object detectors: four SSD MobileNets, one EfficientDet, three YOLO-based detectors and YOLOR. These detectors were deployed and tested on three edge-computing hardware (TPU, CPU and GPU), and evaluated in terms of detection precision and inference time. The results showed that YOLOR was the best trunk detector achieving nearly 90% F1 score and an inference average time of 13.7ms on GPU. This work will favour the development of advanced vision perception systems for robotics in forestry operations.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13