Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning

Abstract

Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA); Class Average Accuracy (CAC); mean Intersection Of the Union (IOU); Precision (P); Recall (R); and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively.

Thickness Classifier on Steel in Heavy Melting Scrap by Deep-learning-based Image Analysis

“…Therefore, the F-score was used as the overall performance index for the developed model, which is defined by the harmonic mean of precision and recall, as expressed in Eq. 1 21) . According to this equation, the Fscores for each class were calculated.…”

Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges

“…Where, 1 -5 denote five key construction scenes on the bridge. 1 denotes construction surveying and proofing, 2 denotes embedding steel casing, 3 denotes erect formwork and stand, 4 denotes steel bar welding and binding, and 5 denotes beam transportation and assembly.…”

“…Most current researchers use deep learning methods in artificial intelligence (AI) for safety monitoring during construction processes [4], with a focus on target detection of construction workers wearing helmets and holding equipment [5]. However, this method ignores the interrelationship between workers and construction objects, leading to a lack of early warning capability for safety monitoring when workers perform non-compliant construction operations.…”

Positioning Errors of Objects Measured by Convolution Neural Network in Unmanned Aerial Vehicle Images

“…When combined, UAVs and CNNs can quickly detect, classify, and locate objects in images. (1)(2)(3) Owing to these advantages, various studies have been conducted in areas such as flood mapping, (4) agriculture, (5)(6)(7)(8)(9) the environment, (10) traffic, (11)(12)(13)(14)(15) structure management, (16)(17)(18)(19)(20) archaeology, (21) and disaster management (22,23) using drones and CNNs.…”