| Full text | |
| Author(s): Show less - |
Nardari, Guilherme V.
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Romero, Roseli A. F.
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Guizilini, Vitor C.
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Mareco, Willy E. C.
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Milori, Debora M. B. P.
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Villas-Boas, Paulino R.
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Dias Santos, Igor Araujo
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DoNascimento, TP
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Colombini, EL
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DeBrito, AV
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Garcia, LTD
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Sa, STD
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Goncalves, LMG
Total Authors: 13
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| Document type: | Journal article |
| Source: | 15TH LATIN AMERICAN ROBOTICS SYMPOSIUM 6TH BRAZILIAN ROBOTICS SYMPOSIUM 9TH WORKSHOP ON ROBOTICS IN EDUCATION (LARS/SBR/WRE 2018); v. N/A, p. 7-pg., 2018-01-01. |
| Abstract | |
Monitoring crops is a time consuming yet important task to ensure production quality. In this paper we present a comparison of convolutional neural network-based methods, evaluating model complexity and performance based on multiple metrics for the binary classification task of segmenting trees from the environment. An Unmanned Aerial Vehicle (UAV) is used to obtain RGB video of orange crops in different altitudes. Keyframes are extracted based on drone trajectory and speed for training and evaluation of the models. The effect on performance of multiple data augmentation techniques is also evaluated. The preferred model is then applied to a reconstruction of a region from multiple images and a color filter is applied for anomaly detection. Experimental and visual results show that these methods are able to segment the environment efficiently without any feature engineering, being a viable pre-processing method for reducing noise in disease identification applications. (AU) | |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/17444-0 - Plantation monitoring using heterogeneous robots |
| Grantee: | Guilherme Vicentim Nardari |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |