Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures

Floods are the most recurrent, widespread and damaging natural disasters, and are ex-pected to become further devastating because of global warming. Blockage of cross-drainage hydraulic structures (e.g., culverts, bridges) by flood-borne debris is an influen-tial factor which usually results in reducing hydraulic capacity, diverting the flows, dam-aging structures and downstream scouring. Australia is among the countries adversely impacted by blockage issues (e.g., 1998 floods in Wollongong, 2007 floods in Newcas-tle). In this context, Wollongong City Council (WCC), under the Australian Rainfall and Runoff (ARR), investigated the impact of blockage on floods and proposed guidelines to consider blockage in the design process for the first time. However, existing WCC guide-lines are based on various assumptions (i.e., visual inspections as representative of hy-draulic behaviour, post-flood blockage as representative of peak floods, blockage remains constant during the whole flooding event), that are not supported by scientific research while also being criticised by hydraulic design engineers. This suggests the need to per-form detailed investigations of blockage from both visual and hydraulic perspectives, in order to develop quantifiable relationships and incorporate blockage into design guide-lines of hydraulic structures. However, because of the complex nature of blockage as a process and the lack of blockage-related data from actual floods, conventional numerical modelling-based approaches have not achieved much success. The research in this thesis applies artificial intelligence (AI) approaches to assess the blockage at cross-drainage hydraulic structures, motivated by recent success achieved by AI in addressing complex real-world problems (e.g., scour depth estimation and flood inundation monitoring). The research has been carried out in three phases: (a) litera-ture review, (b) hydraulic blockage assessment, and (c) visual blockage assessment. The first phase investigates the use of computer vision in the flood management domain and provides context for blockage. The second phase investigates hydraulic blockage using lab scale experiments and the implementation of multiple machine learning approaches on datasets collected from lab experiments (i.e., Hydraulics-Lab Dataset (HD), Visual Hydraulics-Lab Dataset (VHD)). The artificial neural network (ANN) and end-to-end deep learning approaches reported top performers among the implemented approaches and demonstrated the potential of learning-based approaches in addressing blockage is-sues. The third phase assesses visual blockage at culverts using deep learning classifi-cation, detection and segmentation approaches for two types of visual assessments (i.e., blockage status classification, percentage visual blockage estimation). Firstly, a range of existing convolutional neural network (CNN) image classification models are imple-mented and compared using visual datasets (i.e., Images of Culvert Openings and Block-age (ICOB), VHD, Synthetic Images of Culverts (SIC)), with the aim to automate the process of manual visual blockage classification of culverts. The Neural Architecture Search Network (NASNet) model achieved best classification results among those im-plemented. Furthermore, the study identified background noise and simplified labelling criteria as two contributing factors in degraded performance of existing CNN models for blockage classification. To address the background clutter issue, a detection-classification pipeline is proposed and achieved improved visual blockage classification performance. The proposed pipeline has been deployed using edge computing hardware for blockage monitoring of actual culverts. The role of synthetic data (i.e., SIC) on the performance of culvert opening detection is also investigated. Secondly, an automated segmentation-classification deep learning pipeline is proposed to estimate the percentage of visual blockage at circular culverts to better prioritise culvert maintenance. The AI solutions proposed in this thesis are integrated into a blockage assessment framework, designed to be deployed through edge computing to monitor, record and assess blockage at cross-drainage hydraulic structures

Road Pavement Crack Detection Using Deep Learning with Synthetic Data

Robust automatic pavement crack detection is critical to automated road condition evaluation. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this process. This study makes literature review of detection issues of road pavement's distress. The paper considers the existing datasets for detection and segmentation distress of road and asphalt pavement. The work presented in this article focuses on deep learning approach based on synthetic training data generation for segmentation of cracks in the driver-view image. A synthetic dataset generation method is presented, and effectiveness of its applicability to the current problem is evaluated. The relevance of the study is emphasized by research on pixel-level automatic damage detection remains a challenging problem, due to heterogeneous pixel intensity, complex crack topology, poor illumination condition, and noisy texture background

Design of a Real-Time Method for Detection and Evaluation of Corrosion in Vehicles

Automobiles endure several challenges when operating on the road that can degrade their performance, functionality, appearance, and overall utility. Although, corrosion is very ancient, it is the most dangerous hazard to an automobile. Corrosion can be defined as natural interaction between the metal and its surrounding atmosphere which results in oxidation of metal. This leads to change in metal properties and can be severely dangerous. One of the easiest ways to recognize corrosion is by using visual inspection methods. Visual inspection results are highly dependent on the operator’s way of analyzing corrosion and operator’s experience. Thus, visual inspection method lack standardization and is susceptible to human errors. In this research, an automated digital method is proposed to detect the surface corrosion and estimate the damage caused. The new approach has been designed to work effectively irrespective of the illumination levels, image dis-orientation and variance in rust texture. The proposed method in proven to be 96% accurate. Furthermore, the proposed method is designed in the form of a noncommercial, cloud-oriented app which is efficient, fast, low-cost, low-maintenance and possesses global accessibility

Application of Image Processing and Convolutional Neural Networks for Flood Image Classification and Semantic Segmentation

Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and the environment. Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights and biases to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of \u3e9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, South Carolina Department of Transportation (SCDOT) 511 traffic cameras, the US geological Survey (USGS) live river cameras, and images downloaded from search engines. All these images were manually annotated to train the different models and detect a total of eight different object categories. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R-CNN (Region-based CNN), Mask R-CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (efficient object detection) to perform both object detection and segmentation simultaneously. Canny edge detection and aspect ratio concepts are also included in the package for flood water level estimation and classification. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas which can be used to identify flood depth, severity, and risk. “FloodImageClassifier” can be embedded to the USGS live river cameras or 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to decision makers and emergency response authorities in real-time

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202