Communication Dans Un Congrès Année : 2020

Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest

Résumé

Confinement during COVID-19 has caused serious effects on agriculture all over the world. As one of the efficient solutions, mechanical harvest/auto-harvest that is based on object detection and robotic harvester becomes an urgent need. Within the auto-harvest system, robust few-shot object etection model is one of the bottlenecks, since the system is required to deal with new vegetable/fruit categories and the collection of large-scale annotated datasets for all the novel categories is expensive. There are many few-shot object detection models that were developed by the community. Yet whether they could be employed directly for real life agricultural applications is still questionable, as there is a context-gap between the commonly used training datasets and the images collected in real life agricultural scenarios. To this end, in this study, we present a novel cucumber dataset and propose two data augmentation strategies that help to bridge the context-gap. Experimental results show that 1) the state-of- the-art few-shot object detection model performs poorly on the novel ‘cucumber’ category; and 2) the proposed augmentation strategies outperform the commonly used ones.
Fichier principal
Vignette du fichier
2011.02719v1.pdf (8) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03652639 , version 1 (19-02-2025)

Identifiants

Citer

Kevin Riou, Jingwen Zhu, Suiyi Ling, Mathis Piquet, Vincent Truffault, et al.. Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest. 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Sep 2020, Tampere, Finland. pp.1-6, ⟨10.1109/MMSP48831.2020.9287053⟩. ⟨hal-03652639⟩
21 Consultations
0 Téléchargements

Altmetric

Partager

More