Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS

A novel identification procedure from ambient vibration data for buildings of the cultural heritage

Ambient modal identification, also known as Operational Modal Analysis (OMA), aims to identify the modal properties of a structure based on vibration data collected when the structure is under its operating conditions, i.e., no initial excitation or known artificial excitation. This procedure for testing and/or monitoring historic buildings, is particularly attractive for civil engineers concerned with the safety of complex historic structures. However, since the external force is not recorded, the identification methods have to be more sophisticated and based on stochastic mechanics. In this context, this contribution will introduce an innovative ambient identification method based on applying the Hilbert Transform, to obtain the analytical representation of the system response in terms of the correlation function. In particular, it is worth stressing that the analytical signal is a complex representation of a time domain signal: the real part is the time domain signal itself, while the imaginary part is its Hilbert transform. A 3DOF numerical example will be presented to show the accuracy of the proposed procedure, and comparisons with data from other methods assess the reliability of the approach

GPS monitoring of a steel box girder viaduct

Structural performance monitoring of bridges has increased as major infrastructure ages and is required to sustain loads that are significantly greater than those predicted during design. Structural stiffness and/or mass distribution can change over the lifespan of a bridge structure. Resulting changes in profile or resonant frequency provide key indicators of change, and may identify structural defects. Field tests using GPS for monitoring relatively small deformations were carried out on a steel box girder viaduct bridge in the UK. The configuration consisted of five GPS receivers located at key locations on the viaduct and two reference GPS receivers. GPS data was collected at either 10 Hz or 20 Hz and post-processed using proprietary software, along with appropriate filtering and spectral analysis. Three main frequencies were clearly detected by the GPS in the vertical component. A previously reported frequency of approximately 0.56 Hz was identified along with two other frequencies. The peak vertical deflections lie in the range of ± 50 mm, while lateral and longitudinal deflections of much smaller magnitude - in the order of a few mm - are also measured. The use of GPS leads to readily obtained and useful engineering data for continued monitoring of structures

Dynamic testing and long term monitoring of a twelve span viaduct

Peer reviewedPreprin

Comparison of Deep Convolutional Neural Networks and Edge Detectors for Image-Based Crack Detection in Concrete

This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they produced residual noise in the final binary images. The best of these methods was useful in detecting cracks wider than 0.1 mm. DCNNs were used to label images, and accurately labeled them with 99% accuracy. In transfer learning mode, the network accurately detected about 86% of cracked images. DCNNs also detected much finer cracks than edge detection methods. In fully trained and classifier modes, the network detected cracks wider than 0.08 mm; in transfer learning mode, the network was able to detect cracks wider than 0.04 mm. Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process. These results show significant promise for future adoption of DCNN methods for image-based damage detection in concrete. To reduce the residual noise, a hybrid method was proposed by combining the DCNN and edge detectors which reduced the noise by a factor of 24

Detection of postural transitions using machine learning

The purpose of this project is to study the nature of human activity recognition and prepare a dataset from volunteers doing various activities which can be used for constructing the various parts of a machine learning model which is used to identify each volunteers posture transitions accurately. This report presents the problem definition, equipment used, previous work in this area of human activity recognition and the resolution of the problem along with results. Also this report sheds light on the process and the steps taken to undertake this endeavour of human activity recognition such as building of a dataset, pre-processing the data by applying filters and various windowing length techniques, splitting the data into training and testing data, performance of feature selection and feature extraction and finally selecting the model for training and testing which provides maximum accuracy and least misclassification rates. The tools used for this project includes a laptop equipped with MATLAB and EXCEL and MEDIA PLAYER CLASSIC respectively which have been used for data processing, model training and feature selection and Labelling respectively. The data has been collected using an Inertial Measurement Unit contains 3 tri-axial Accelerometers, 1 Gyroscope, 1 Magnetometer and 1 Pressure sensor. For this project only the Accelerometers, Gyroscope and the Pressure sensor is used. The sensor is made by the members of the lab named ‘The Technical Research Centre for Dependency Care and Autonomous Living (CETpD) at the UPC-ETSEIB campus. The results obtained have been satisfactory, and the objectives set have been fulfilled. There is room for possible improvements through expanding the scope of the project such as detection of chronic disorders or providing posture based statistics to the end user or even just achieving a higher rate of sensitivity of transitions of posture by using better features and increasing the dataset size by increasing the number of volunteers.Incomin

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

Enhancing structural health monitoring with vehicle identification and tracking

Traffic load monitoring and structural health monitoring (SHM) have been gaining increasing attention over the last decade. However, most of the current installations treat the two monitoring types as separated problems, thereby using dedicated installed sensors, such as smart cameras for traffic load or accelerometers for Structural Health Monitoring (SHM). This paper presents a new framework aimed at leveraging the data collected by a SHM system for a second use, namely, monitoring vehicles passing on the structure being monitored (a viaduct). Our framework first processes the raw three-axial acceleration signals through a series of transformations and extracts its energy. Then, an anomaly detection algorithm is used to detect peaks from 90 installed sensors, and a linear regression together with a simple threshold filters out false detection by estimating the speed of the vehicles. Initial results in conditions of moderate traffic load are promising, demonstrating the detection of vehicles and realistic characterization of their speed. Moreover, a k-means clustering analysis distinguishes two groups of peaks with statistically different features such as amplitude and damping duration that could be likely associated with heavy vehicles and cars, respectively

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS