Vineyard trunk detection using deep learning - An experimental device benchmark

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Pinto de Aguiar, ASP (Autor) Outra; Filipe Neves Santos (Autor) FEUP; Feliz dos Santos, LCF (Autor) Outra; de Jesus Filipe, VMD (Autor) Outra; Armando Jorge Sousa (Autor) FEUP

Revista

Título: Computers and Electronics in Agriculture

Vol. 175

ISSN: 0168-1699

Editora: Elsevier

Indexação

ISI Web of Knowledge - 43 Citações

Scopus - 51 Citações

Outras Informações

ID Authenticus: P-00S-A51

DOI: 10.1016/j.compag.2020.105535

Abstract (EN): Research and development in mobile robotics are continuously growing. The ability of a human-made machine to navigate safely in a given environment is a challenging task. In agricultural environments, robot navigation can achieve high levels of complexity due to the harsh conditions that they present. Thus, the presence of a reliable map where the robot can localize itself is crucial, and feature extraction becomes a vital step of the navigation process. In this work, the feature extraction issue in the vineyard context is solved using Deep Learning to detect high-level features - the vine trunks. An experimental performance benchmark between two devices is performed: NVIDIA's Jetson Nano and Google's USB Accelerator. Several models were retrained and deployed on both devices, using a Transfer Learning approach. Specifically, MobileNets, Inception, and lite version of You Only Look Once are used to detect vine trunks in real-time. The models were retrained in a built in-house dataset, that is publicly available. The training dataset contains approximately 1600 annotated vine trunks in 336 different images. Results show that NVIDIA's Jetson Nano provides compatibility with a wider variety of Deep Learning architectures, while Google's USB Accelerator is limited to a unique family of architectures to perform object detection. On the other hand, the Google device showed an overall Average precision higher than Jetson Nano, with a better runtime performance. The best result obtained in this work was an average precision of 52.98% with a runtime performance of 23.14 ms per image, for MobileNet-V2. Recent experiments showed that the detectors are suitable for the use in the Localization and Mapping context.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9