Adaptive Road Crack Detection System by Pavement Classification

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement

Integrating Pavement Crack Detection and Analysis Using Autonomous Unmanned Aerial Vehicle Imagery

Efficient, reliable data is necessary to make informed decisions on how to best manage aging road assets. This research explores a new method to automate the collection, processing, and analysis of transportation networks using Unmanned Aerial Vehicles and Computer Vision technology. While there are current methodologies to accomplish road assessment manually and semi-autonomously, this research is a proof of concept to obtain the road assessment faster and cheaper with a vision for little to no human interaction required. This research evaluates the strengths of applying UAV technology to pavement assessments and identifies where further work is needed. Furthermore, it validates using UAVs as a viable way forward for collecting pavement information to aid asset managers in sustaining aging road assets. The system was able to capture road photos suitable for semi automated Pavement Condition Index (PCI) processing, however the algorithm resulted in a maximum F-Measure of 40%. This result is low and indicates the algorithm is not sufficient for fully automated PCI classification. Accurately detecting road defects using computer vision remains a challenging problem for future research. However, using Autonomous UAVs to collect the data is a viable avenue for data collection, theoretically faster than current methods at freeway speeds

A new minimal path selection algorithm for automatic crack detection on pavement images

International audienceThis paper proposes a new algorithm for crack detection based on the selection of minimal paths. It takes account of both photometric and geometric characteristics and requires few information a priori. It is validated on synthetic and real images

Techniques for automatic large scale change analysis of temporal multispectral imagery

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst\u27s job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots

This paper describes a novel method of training a semantic segmentation model for environment recognition of agricultural mobile robots by unsupervised domain adaptation exploiting publicly available datasets of outdoor scenes that are different from our target environments i.e., greenhouses. In conventional semantic segmentation methods, the labels are given by manual annotation, which is a tedious and time-consuming task. A method to work around the necessity of the manual annotation is unsupervised domain adaptation (UDA) that transfer knowledge from labeled source datasets to unlabeled target datasets. Most of the UDA methods of semantic segmentation are validated by tasks of adaptation from non-photorealistic synthetic images of urban scenes to real ones. However, the effectiveness of the methods is not well studied in the case of adaptation to other types of environments, such as greenhouses. In addition, it is not always possible to prepare appropriate source datasets for such environments. In this paper, we adopt an existing training method of UDA to a task of training a model for greenhouse images. We propose to use multiple publicly available datasets of outdoor images as source datasets, and also propose a simple yet effective method of generating pseudo-labels by transferring knowledge from the source datasets that have different appearance and a label set from the target datasets. We demonstrate in experiments that by combining our proposed method of pseudo-label generation with the existing training method, the performance was improved by up to 14.3% of mIoU compared to the best score of the single-source training.Comment: 10 pages, 7 figures, submitted to Machine Vision And Application

Connectionist systems for image processing and anomaly detection

Dissertação de mestrado integrado em Engenharia InformáticaA Inteligência Artificial (IA) e a Ciência de Dados estão cada vez mais presentes no nosso quotidiano e os benefícios que trouxeram para a sociedade nos últimos anos são notáveis. O sucesso da IA foi impulsionado pela capacidade adaptativa que as máquinas adquiriram e está estreitamente relacionada com a sua habilidade para aprender. Os sistemas conexionistas, apresentados na forma de Redes Neurais Artificiais (RNAs), que se inspiram no sistema nervoso humano, são um dos mais importantes modelos que permitem a aprendizagem. Estes são utilizados em diversas áreas, como em problemas de previsão ou classificação, apresentando resultados cada vez mais satisfatórios. Uma das áreas em que esta tecnologia se tem destacado é a Visão Computacional (Computer Vision (CV)), permitindo, por exemplo, a localização de objetos em imagens e a sua correta identificação. A Deteção de Anomalias (Anomaly Detection (AD)) é outro campo onde as RNAs vêm surgindo como uma das tecnologias para a resolução de problemas. Em cada área são utilizadas diferentes arquiteturas de acordo com o tipo de dados e o problema a resolver. Combinando o processamento de imagens e a deteção de anomalias, verifica-se uma convergência de metodologias que utilizam módulos convolucionais em arquiteturas dedicadas a AD. O objetivo principal desta dissertação é estudar as técnicas existentes nestes domínios, desenvolvendo diferentes arquiteturas e modelos, aplicando-as a casos práticos de forma a comparar os resultados obtidos em cada abordagem. O caso prático principal consiste na monitorização de pavimentos rodoviários por meio de imagens para a identificação automática de áreas degradadas. Para isso, dois protótipos de software são propostos para recolher e visualizar os dados adquiridos. O estudo de arquiteturas de RNAs para o diagnóstico da condição do asfalto por meio de imagens é o foco central no processo científico apresentado. Os métodos de Machine Learning (ML) utilizados incluem classificadores binários, Autoencoders (AEs) e Variational Autoencoders (VAEs). Para os dois últimos modelos, práticas supervisionadas e não supervisionadas são também comparadas, comprovando a sua utilidade em cenários onde não há dados rotulados disponíveis. Usando o modelo VAE num ambiente supervisionado, este apresenta uma excelente distinção entre áreas de pavimentação em boas condições e degradadas. Quando não existem dados rotulados disponíveis, a melhor opção é utilizar o modelo AE, utilizando a distribuição de semelhanças das reconstruções para calcular o threshold de separação, atingindo accuracy e precision superiores a 94%). O processo completo de desenvolvimento mostra que é possível construir uma solução alternativa para diminuir os custos de operação em relação aos sistemas comerciais existentes e melhorar a usabilidade quando comparada às soluções tradicionais. Adicionalmente, dois estudos demonstram a versatilidade dos sistemas conexionistas na resolução de problemas, nomeadamente no projeto de estruturas mecânicas, possibilitando a modelação de campos de deslocamento e pressão em placas reforçadas; e na utilização de AD para identificar locais de aglomeração de pessoas através de técnicas de crowdsensing.Artificial Intelligence (AI) and Data Science (DS) have become increasingly present in our daily lives, and the benefits it has brought to society in recent years are remarkable. The success of AI was driven by the adaptive capacity that machines gained, and it is closely related to their ability to learn. Connectionist systems, presented in the form of Artificial Neural Networks (ANNs), which are inspired by the human nervous system, are one of the principal models that allows learning. These models are used in several areas, like forecasting or classification problems, presenting increasingly satisfactory results. One area in which this technology has excelled is Com puter Vision (CV), allowing, for example, the location of objects in images and their correct identification. Anomaly Detection (AD) is another field where ANNs have been emerging as one technology for problem solving. In each area, different architectures are used according to the type of data and the problem to be solved. Combining im age processing and the finding of anomalies in this type of data, there is a convergence of methodologies using convolutional modules in architectures dedicated to AD. The main objective of this dissertation is to study the existent techniques in these domains, developing different model architectures, and applying them to practical case studies in order to compare the results obtained in each approach. The major practical use case consists of monitoring road pavements using images to automatically identify degraded areas. For that, two software prototypes are proposed to gather and visualise the acquired data. Moreover, the study of ANN architectures to diagnose the asphalt condition through images is the central focus of this work. The experimented methods for AD in images include a binary classifier network as a baseline, Autoencoders (AEs) and Variational Autoen coders (VAEs). Supervised and unsupervised practises are also compared, proving their utility also in scenarios where there is no labelled data available. Using the VAE model in a supervised setting, it presents a excellent distinction between good and bad pavement areas. When labelled data is not available, using the AE and the distribution of similarities of good pavement reconstructions to calculate the threshold is the best option with both accuracy and precision above 94%. The full development process shows it is possible to build an alternative solution to decrease the operation costs relatively to expensive commercial systems and improve usability when compared with traditional solutions. Additionally, two case studies demonstrate the versatility of connectionist systems to solve problems, namely in Mechanical Structural Design enabling the modelling of displacement and pressure fields in reinforced plates; and using AD to identify crowded places through crowd-sensing techniques