A Deep Learning Approach to Vegetation Images Recognition in Buildings: a Hyperparameter Tuning Case Study


  • André Luiz Carvalho Ottoni Programa de P´os-Graduac¸ ˜ao em Engenharia El´etrica da Universidade Federal da Bahia (UFBA), Salvador (BA) / Centro de Ciˆencias Exatas e Tecnol´ogicas da Universidade Federal do Recˆoncavo da Bahia (UFRB) https://orcid.org/0000-0003-2136-9870
  • Marcela Silva Novo Departamento de Engenharia El´etrica e de Computac¸ ˜ao, Universidade Federal da Bahia (UFBA) https://orcid.org/0000-0003-2742-3145


Deep Learning, Convolutional Neural Networks, Vegetation images recognition, hyperparameter tuning


Deep Learning methods have important applications in digital image processing. However, the literature lacks further studies that propose machine learning models to images classification in civil construction area. For example, the vegetation recognition on facades can be relevant in identifying the degradation and abandonment of buildings. Thus, the objective of this paper is to propose an Convolutional Neural Networks (CNN) approach to vegetation images recognition in buildings. For this, a database with urban images (low altitude) captured by a drone in Zurich (Switzerland) was adopted. In addition, a rigorous hyperparameters tuning methodology for the CNN model is presented. After adjusting the hyperparameters and the final model, the system achieved 90% of accuracy in the test stage. It should also be noted that CNN correctly classified 97.8% of the positive class (with vegetation on the facade) in test images.


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Author Biographies

André Luiz Carvalho Ottoni, Programa de P´os-Graduac¸ ˜ao em Engenharia El´etrica da Universidade Federal da Bahia (UFBA), Salvador (BA) / Centro de Ciˆencias Exatas e Tecnol´ogicas da Universidade Federal do Recˆoncavo da Bahia (UFRB)

Andr´e Luiz Carvalho Ottoni possui graduac¸ ˜ao (2015) e mestrado (2016) em Engenharia El´etrica pela Universidade Federal de S˜ao Jo˜ao del-Rei (UFSJ). Atualmente ´e aluno de doutorado no Programa de P´os-Graduac¸ ˜ao em Engenharia El´etrica da Universidade Federal da Bahia (UFBA). Tamb´em ´e professor Assistente no Centro de Ciˆencias Exatas e Tecnol´ogicas (CETEC) da Universidade Federal do Recˆoncavo da Bahia (UFRB) e membro da Sociedade Brasileira de Autom´atica (SBA). T´opicos de pesquisa: Inteligˆencia Artificial, Aprendizado de M´aquina, Otimizac¸ ˜ao Combinat´oria e Rob´otica Inteligente.

Marcela Silva Novo, Departamento de Engenharia El´etrica e de Computac¸ ˜ao, Universidade Federal da Bahia (UFBA)

Marcela Silva Novo possui graduac¸ ˜ao em Engenharia de Telecomunicac¸ ˜oes pela Universidade Federal Fluminense (2001), mestrado em Engenharia El´etrica pela Pontif´ıcia Universidade Cat´olica do Rio de Janeiro (2003) e doutorado em Engenharia El´etrica pela Pontif´ıcia Universidade Cat´olica do Rio de Janeiro (2007). De 2005 a 2006 foi pesquisadora visitante no ElectroScience Laboratory, The Ohio State University, USA. Atualmente ´e professora associada e vice-diretora da Escola Polit´ecnica da UFBA. ´E membro da Diretoria Executiva da Sociedade Brasileira de Microondas e Optoeletrˆonica. T´opicos de pesquisa: eletromagnetismo computacional, m´etodos num´ericos, an´alise e s´ıntese de dispositivos de microondas e antenas, e processamento de sinais.


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How to Cite

Ottoni, A. L. C., & Novo, M. S. (2021). A Deep Learning Approach to Vegetation Images Recognition in Buildings: a Hyperparameter Tuning Case Study. IEEE Latin America Transactions, 19(12), 2062–2070. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5075

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