Small datasets for fruit detection with transfer learning

Dai, Dan, Gao, Junfeng, Parsons, Simon and Sklar, Elizabeth (2021) Small datasets for fruit detection with transfer learning. In: 4th UK-RAS Conference, June 2nd 2021, Online.

Full content URL: http://doi.org/10.31256/Nf6Uh8Q

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Small datasets for fruit detection with transfer learning
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Item Type:Conference or Workshop contribution (Paper)
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Abstract

A common approach to the problem of fruit detection in images is to design a deep learning network and train a model to locate objects, using bounding boxes to identify regions containing fruit. However, this requires sufficient data and presents challenges for small datasets. Transfer learning, which acquires knowledge from a source domain and brings that to a new target domain, can produce improved performance in the target domain. The work discussed in this paper shows the application of transfer learning for fruit detection with small datasets and presents analysis between the number of training images in source and target domains.

Keywords:Fruit detection, Limited datasets, Transfer learning, Target domain
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
D Veterinary Sciences, Agriculture and related subjects > D410 Arable and Fruit Farming
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:46542
Deposited On:05 Oct 2021 13:43

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