| Texto completo | |
| Autor(es): |
Número total de Autores: 4
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| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Math & Comp Sci, Av Trabalhador Sao Carlense 400, BR-13560970 Sao Carlos, SP - Brazil
[2] Nitryx Consulting, Rua Barao Jaguara 1481, Sala 146, BR-13015910 Campinas, SP - Brazil
[3] Univ Newcastle, Sch Math & Phys Sci, Univ Dr, Callaghan, NSW 2308 - Australia
Número total de Afiliações: 3
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| Tipo de documento: | Artigo Científico |
| Fonte: | COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 189, OCT 2021. |
| Citações Web of Science: | 0 |
| Resumo | |
Among the production areas with largest impact on global economy, agriculture and livestock play a prominent role. Technologies have been developed in order to automate and increase the efficiency of these fields. The use of Unmanned Aerial Vehicles (UAVs) has been extensively investigated to improve the efficiency of agricultural production and in the monitoring of animals. One of the most important and challenging tasks in animal monitoring is cattle counting. In this paper, we propose a method for detecting and counting cattle in aerial images obtained by UAVs, based on Convolutional Neural Networks (CNNs) and a graph-based optimization to remove duplicated animals detected in overlapped images. We show that maximizing the degree of matching between animals is a suitable strategy to reduce duplicate counting. We also offer a dataset of real images, obtained from large pasture areas, both for training as well as for testing/benchmarking of cattle counting techniques. Our results show that the proposed method is very competitive, outperforming the state of the art in detecting duplicated animals, while significantly reducing the computational cost of the overall counting task. (AU) | |
| Processo FAPESP: | 18/22482-0 - Aprendendo características de conteúdo visual sob condições de supervisão limitada utilizando múltiplos domínios |
| Beneficiário: | Moacir Antonelli Ponti |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |