de Araujo Thabatta Moreira Alves, Teixeira Carlos André de Mattos, Francês Carlos Renato Lisboa
High Performance Network Planning Laboratory, Federal University of Pará, Belém, Pará, Brazil.
Departament of Computing, Federal Center for Technological Education of Minas Gerais, Divinópolis, Minas Gerais, Brazil.
PeerJ Comput Sci. 2024 Aug 12;10:e2052. doi: 10.7717/peerj-cs.2052. eCollection 2024.
Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.
大多数自然灾害是由山体滑坡和边坡坍塌等地壳动力事件引起的。这些灾害会造成直接影响环境并导致经济和人员损失的灾难。目视检查是检测岩土结构故障的主要方法，但由于土壤不稳定，现场勘查可能存在风险。此外，物体设计以及恶劣和偏远的安装条件使得对这些结构进行监测变得不可行。当需要快速且安全的评估时，通过计算方法进行分析就变得可行。在本研究中，一种用于计算机视觉的卷积神经网络（CNN）方法被应用于在无人机（UAV）和移动设备的辅助下识别岩土结构表面的缺陷，旨在减少对人工主导的现场检查的依赖。然而，由于岩土工程的特殊性，如公共数据集有限和图像冗余，该领域在计算机视觉算法方面的研究仍有待探索。因此，本研究在无人机和移动设备的辅助下，获取了巴西一条国道附近边坡表面破坏指标的图像。然后，我们提出了一种定制的CNN和低复杂度模型架构，以构建一个用于检测岩土表面故障的图像辅助二分类器。该模型取得了令人满意的平均准确率94.26%。来自接收者操作特征（ROC）曲线的AUC度量分数为0.99，以及与测试数据集的混淆矩阵显示了令人满意的结果。结果表明，该模型区分“损坏”和“完好”类别的能力非常出色。它能够识别破坏指标。边坡表面早期破坏指标的检测可以促进适当的维护和预警，并预防灾害，因为土壤的完整性直接影响其周围和上方建造的结构。
