Efficient Evaluation of the Number of False Alarm Criterion

This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds, and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion, and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results, and underlines its potential for handling complex pattern detection tasks

Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with background textures besides the lack of distinct features in cracks. Hyperspectral images are sensitive to surface material changes and their potential for road crack detection is explored here. The key observation is that road cracks reveal the interior material that is different from the worn surface material. A novel asphalt crack index is introduced here as an additional clue that is sensitive to the spectra in the range 450–550 nm. The crack index is computed and found to be strongly correlated with the appearance of fresh asphalt cracks. The new index is then used to differentiate cracks from road surfaces. Several experiments have been made, which confirmed that the proposed index is effective for crack detection. The recall-precision analysis showed an increase in the associated F1-score by an average of 21.37% compared to the VIS2 metric in the literature (a metric used to classify pavement condition from hyperspectral data)

Computer vision-based structural assessment exploiting large volumes of images

Visual assessment is a process to understand the state of a structure based on evaluations originating from visual information. Recent advances in computer vision to explore new sensors, sensing platforms and high-performance computing have shed light on the potential for vision-based visual assessment in civil engineering structures. The use of low-cost, high-resolution visual sensors in conjunction with mobile and aerial platforms can overcome spatial and temporal limitations typically associated with other forms of sensing in civil structures. Also, GPU-accelerated and parallel computing offer unprecedented speed and performance, accelerating processing the collected visual data. However, despite the enormous endeavor in past research to implement such technologies, there are still many practical challenges to overcome to successfully apply these techniques in real world situations. A major challenge lies in dealing with a large volume of unordered and complex visual data, collected under uncontrolled circumstance (e.g. lighting, cluttered region, and variations in environmental conditions), while just a tiny fraction of them are useful for conducting actual assessment. Such difficulty induces an undesirable high rate of false-positive and false-negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome the inherent challenges in using such images for visual assessment, high-level computer vision algorithms must be integrated with relevant prior knowledge and guidance, thus aiming to have similar performance with those of humans conducting visual assessment. Moreover, the techniques must be developed and validated in the realistic context of a large volume of real-world images, which is likely contain numerous practical challenges. In this dissertation, the novel use of computer vision algorithms is explored to address two promising applications of vision-based visual assessment in civil engineering: visual inspection, and visual data analysis for post-disaster evaluation. For both applications, powerful techniques are developed here to enable reliable and efficient visual assessment for civil structures and demonstrate them using a large volume of real-world images collected from actual structures. State-of-art computer vision techniques, such as structure-from-motion and convolutional neural network techniques, facilitate these tasks. The core techniques derived from this study are scalable and expandable to many other applications in vision-based visual assessment, and will serve to close the existing gaps between past research efforts and real-world implementations

Robotic navigation and inspection of bridge bearings

This thesis focuses on the development of a robotic platform for bridge bearing inspection. The existing literature on this topic highlights an aspiration for increased automation of bridge inspection, due to an increasing amount of ageing infrastructure and costly inspection. Furthermore, bridge bearings are highlighted as being one of the most costly components of the bridge to maintain. However, although autonomous robotic inspection is often stated as an aspiration, the existing literature for robotic bridge inspection often neglects to include the requirement of autonomous navigation. To achieve autonomous inspection, some methods for mapping and localising in the bridge structure are required. This thesis compares existing methods for simultaneous localisation and mapping (SLAM) with localisation-only methods. In addition, a method for using pre-existing data to create maps for localisation is proposed. A robotic platform was developed and these methods for localisation and mapping were then compared in a laboratory environment and then in a real bridge environment. The errors in the bridge environment are greater than in the laboratory environment, but remained within a defined error bound. A combined approach is suggested as an appropriate method for combining the lower errors of a SLAM approach with the advantages of a localisation approach for defining existing goals. Longer-term testing in a real bridge environment is still required. The use of existing inspection data is then extended to the creation of a simulation environment, with the goal of creating a methodology for testing different configurations of bridges or robots in a more realistic environment than laboratory testing, or other existing simulation environments. Finally, the inspection of the structure surrounding the bridge bearing is considered, with a particular focus on the detection and segmentation of cracks in concrete. A deep learning approach is used to segment cracks from an existing dataset and compared to an existing machine learning approach, with the deep-learning approach achieving a higher performance using a pixel-based evaluation. Other evaluation methods were also compared that take the structure of the crack, and other related datasets, into account. The generalisation of the approach for crack segmentation is evaluated by comparing the results of the trained on different datasets. Finally, recommendations for improving the datasets to allow better comparisons in future work is given

Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

Autonomous inspection of roads is gaining interest to improve the efficiency of road repair and maintenance. In this paper we will be showing the potential for using Hyper Spectral Cameras, HSC, to identify road defects. The key idea of this paper is that cracks in the road show the interior material of road pavement which have different chemical composition from the surface materials due to surface wear. Material changes of the road surface give rise to a spectral signature that can be easily detected in HSC images. This condition facilitates the detection of cracks and potholes, which can be difficult if working in the visible spectrum domain only. We report on experiments with a HSC to identify the road material changes and their association to cracks and potholes. A new metric is devised to measure the amount of metal oxides and associate its absence to the appearance of cracks. The metric is shown to be more discriminative than previous indicators in the literature

Combining Block-based and Pixel-based Approaches to Improve Crack Detection and Localisation

A variety of civil engineering applications require the identification of cracks in roads and buildings. In such cases, it is frequently helpful for the precise location of cracks to be identified as labelled parts within an image to facilitate precision repair for example. CrackIT is known as a crack detection algorithm that allows a user to choose between a block-based or a pixel-based approach. The block-based approach is noise-tolerant but is not accurate in edge localization while the pixel-based approach gives accurate edge localisation but is not noise-tolerant. We propose a new approach that combines both techniques and retains the advantages of each. The new method is evaluated on three standard crack image datasets. The method was compared with the CrackIT method and three deep learning methods namely, HED, RCF and the FPHB. The new approach outperformed the existing arts and reduced the discretisation errors significantly while still being noise-tolerant

Development of artificial neural network-based object detection algorithms for low-cost hardware devices

Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers. This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models. The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware