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.

中文翻译：

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建筑物植被图像识别的深度学习方法：超参数调整案例研究


深度学习方法在数字图像处理中具有重要的应用。然而，文献缺乏提出土木建筑领域图像分类机器学习模型的进一步研究。例如，立面上的植被识别可以与识别建筑物的退化和废弃相关。因此，本文的目的是提出一种卷积神经网络（CNN）方法来识别建筑物中的植被图像。为此，采用了由无人机在苏黎世（瑞士）拍摄的城市图像（低空）的数据库。此外，还提出了 CNN 模型的严格超参数调整方法。经过调整超参数和最终模型后，系统在测试阶段达到了90%的准确率。还应该指出的是，CNN 正确分类了测试图像中 97.8% 的正类（正面有植被）。