Automatic Detection of Road Cracks using EfficientNet with Residual U-Net-based Segmentation and YOLOv5-based Detection

The main factor affecting road performance is pavement damage. One of the difficulties in maintaining roads is pavement cracking. Credible and reliable inspection of heritage structural health relies heavily on crack detection on road surfaces. To achieve intelligent operation and maintenance, intelligent crack detection is essential to traffic safety. The detection of road pavement cracks using computer vision has gained popularity in recent years. Recent technological breakthroughs in general deep learning algorithms have resulted in improved results in the discipline of crack detection. In this paper, two techniques for object identification and segmentation are proposed. The EfficientNet with residual U-Net technique is suggested for segmentation, while the YOLO v5 algorithm is offered for crack detection. To correctly separate the pavement cracks, a crack segmentation network is used. Road crack identification and segmentation accuracy were enhanced by optimising the model's hyperparameters and increasing the feature extraction structure. The suggested algorithm's performance is compared to state-of-the-art algorithms. The suggested work achieves 99.35% accuracy

CONCRETE CRACK EVALUATION FOR CIVIL INFRASTRUCTURE USING COMPUTER VISION AND DEEP LEARNING

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Surface cracks of civil infrastructure are one of the important indicators for structural durability and integrity. Concrete cracks are typically investigated by manual visual observation on the surface, which is intrinsically subjective because it highly depends on the experience of inspectors. Furthermore, manual visual inspection is time-consuming, expensive, and often unsafe when inaccessible structural components need to be assessed. Computer vision-based approach is recognized as a promising alternative that can automatically extract crack information from images captured by the digital camera. As texts and cracks are similar in terms of consisting distinguishable lines and curves, image binarization developed for text detection can be appropriate for crack identification purposes. However, although image binarization is useful to separate cracks and backgrounds, the crack assessment is difficult to standardize owing to the high dependence of binarization parameters determined by users. Another critical challenge in digital image processing for crack detection is to automatically distinguish cracks from an image containing actual cracks and crack-like noise patterns (e.g., stains, holes, dark shadows, and lumps), which are often seen on the surface of concrete structures. In addition, a tailored camera system and the corresponding strategy are necessary to effectively address the practical issues in terms of the skewed angle and the process of the sequential crack images for efficient measurement. This research develops a computer vision-based approach in conjunction with deep learning for accurate crack evaluation of for civil infrastructure. The main contribution of the proposed approach can be summarized as follows: (1) a deep learning-based approach for crack detection, (2) a hybrid image processing for crack quantification, and (3) camera systems for the practical issues on civil infrastructure in terms of a skewed angle problem and an efficient measurement with the sequential crack images. The proposed research allows accurate crack evaluation to provide a proper maintenance strategy for civil infrastructure in practice.clos

Modeling and Compensating of Noise in Time-of-Flight Sensors

Three-dimensional (3D) sensors provide the ability to perform contactless measurements of objects and distances that are within their field of view. Unlike traditional two-dimensional (2D) cameras, which only provide RGB data about objects within a scene, 3D sensors are able to directly provide depth information for objects within a scene. Of these 3D sensing technologies, Time-of-Flight (ToF) sensors are becoming more compact which allows them to be more easily integrated with other devices and to find use in more applications. ToF sensors also provide several benefits over other 3D sensing technologies that increase the types of applications where ToF sensors can be used. For example, over the last decade, ToF sensors have become more widely used in applications such as 3D scanning, drone positioning, robotics, logistics, structural health monitoring, and road surveillance. To further extend the applications where ToF sensors can be employed, this work focuses on how to improve the performance of ToF sensors by suppressing and mitigating the effects of noise artifacts that are associated with ToF sensors. These issues include multipath interference, motion blur, and multicamera interference in 3D depth maps and point clouds

Surface and Sub-Surface Analyses for Bridge Inspection

The development of bridge inspection solutions has been discussed in the recent past. In this dissertation, significant development and improvement on the state-of-the-art in the field of bridge inspection using multiple sensors (e.g. ground penetrating radar (GPR) and visual sensor) has been proposed. In the first part of this research (discussed in chapter 3), the focus is towards developing effective and novel methods for rebar detection and localization for sub-surface bridge inspection of steel rebars. The data has been collected using Ground Penetrating Radar (GPR) sensor on real bridge decks. In this regard, a number of different approaches have been successively developed that continue to improve the state-of-the-art in this particular research area. The second part (discussed in chapter 4) of this research deals with the development of an automated system for steel bridge defect detection system using a Multi-Directional Bicycle Robot. The training data has been acquired from actual bridges in Vietnam and validation is performed on data collected using Bicycle Robot from actual bridge located in Highway-80, Lovelock, Nevada, USA. A number of different proposed methods have been discussed in chapter 4. The final chapter of the dissertation will conclude the findings from the different parts and discuss ways of improving on the existing works in the near future

Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

Computer Vision-Based Automatic Railroad Crossing Monitoring and Track Inspection

Currently, there are many imminent challenges in the railroad infrastructure system of the United States, impacting the operation, safety, and management of railroad transportation. In this work, three major challenges which are overcrowded traffic congestion at the grade crossing, low-efficiency and accuracy on inspection of missing or broken rail track components, and dense rail surface defects without quantification, respectively are studied. The congested railroad grade crossing not only introduces significant traffic delays to travelers but also brings potential safety concerns to the first responders. However, limited studies have been devoted on developing an intelligent traffic monitoring system which is significant to deliver real-time information to the travelers and the first responders to improve the traffic operation and safety at the railroad grade crossing. Except to improve the railroad safety related with travelers and the first responders in the first half, the rest of this dissertation focuses on the track safety related to railroad track components and surface defects. The missing or broken components such as spikes, clips, and tie plates can endanger the safety and operation of railroads. Even though various types of inspection approaches such as ground penetrating radar, laser, and LiDAR have been implemented, the operation needs rich experience and extensive training. Meanwhile, track inspections still heavily rely on manual inspection which is low-accurate, low-efficient, and highly subjective. Moreover, rail surface defects negatively impact riding comfort, operational safety, and could even lead to train derailments. During the past decades, there have been many efforts to detect rail surface defects. Unfortunately, previous approaches for detecting and quantifying of rail surface defects are also limited by the high requirements of specialized equipment and personnel training. The main focus of this work is to design and develop computer vision models to address the technical and practical challenges mentioned above. To cope with each challenge, different models including the object detection model, the instance segmentation model, and the semantic segmentation model have been successfully designed and developed. To train, validate, and test different models, three customized image datasets based on the traffic videos at the grade crossing, railroad component images, and dense rail surface defects images have been built. Specifically, a dense traffic detection net (DTDNet) is developed integrating the Transformer Attention (TA) module for better modeling of global context information and the learning-to-match detection head for optimizing object detection and localization using a likelihood probability fashion. A unique grade crossing traffic image dataset including congested and normal traffic during both daytime and nighttime is established. The proposed DTDNet and other state-of-the-art (SOTA) models have been trained, tested, and compared. The proposed DTDNet outperforms other SOTA models in the test cases. Regarding the automatic track components inspection, the real-time instance segmentation model and the YOLOv4-hybrid model have been designed, trained, tested, and evaluated. The first public rail components image database has been built and released online. Compared to the original YOLACT model and the Mask R-CNN model, the training performance has been improved with the improved instance segmentation model. The detection accuracy on the bounding box and the mask has been improved and the inference speed can achieve the real-time speed. With respect to the YOLOv4-hybrid model, it outperforms other SOTA models on the training performance and the field tests with missing or fake rail track components. As for the rail surface defect inspection and quantification, the optimized Mask R-CNN model and the newly proposed lightweight Deeplabv3Plus model using Lovász-Softmax loss (LDL model) have been trained, tested, evaluated, and compared on our rail surface defects image database. Experimental results confirm the robustness and superiority of our model on defect segmentation. Besides, an algorithm is proposed to quantify rail surface defect severities at different levels using our rail surface defects image data. Overall, this dissertation helps to improve the railroad safety by developing and implementing advanced computer vision-based models for better tracking monitoring and inspections