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Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest
2020 Ieee 22nd International Workshop on Multimedia Signal Processing ; 2020.
Article in English | Web of Science | ID: covidwho-1261630
ABSTRACT
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 detection 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.
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Collection: Databases of international organizations Database: Web of Science Type of study: Case report Language: English Journal: 2020 Ieee 22nd International Workshop on Multimedia Signal Processing Year: 2020 Document Type: Article

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Collection: Databases of international organizations Database: Web of Science Type of study: Case report Language: English Journal: 2020 Ieee 22nd International Workshop on Multimedia Signal Processing Year: 2020 Document Type: Article