Most natural disasters result from geodynamic events, such as landslides and collapse of geotechnical structures. These failures are catastrophic that directly impact the environment and cause financial and human losses. Visual inspection is the main method for detecting surface flaws in geotechnical structures. However, visits to the site can be risky due to the possibility of soil’s instability. Furthermore, the terrain design, hostile environment and remote installation conditions make access to these structures impractical. When a quick and safe assessment is necessary, computer vision analysis becomes a potential alternative. However, studies on computer vision techniques still need to be explored in this field due to the particularities of geotechnical engineering, such as limited, redundant and scarce public data sets. In this context, this thesis presents a redes neurais convolucionais, do inglês Convolutional Neural Network (CNN) approach for identifying defects on the surface of geotechnical structures to reduce dependence on human-led on-site inspections. To this end, images of surface failure indicators were collected on slopes on the banks of a Brazilian highway, with the help of UAVs and mobile devices. Next, low-complexity CNN architectures were explored to build a binary classifier capable of detecting flaws apparent to the naked human eye in images. The architecture composed of three convolutional layers, each with 32 filters, followed by two fully connected layers, each composed of 128 neurons and output with one neuron, showed an accuracy of 94.26%. The performance evaluation of the model with the test set obtained AUC metrics of 0.99, confusion matrix, and a AUPRC curve that indicates robust performance of the classifier in detecting damage, while maintaining a low computational complexity, making it suitable for applications field practices. The contributions of the thesis include the provision of an image database, the obtaining of a classification model suitable for scarce data and limited computational resources, and the exploration of strategies for remote inspection and detection of signs of failure in geotechnical structures.
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