Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

Pixel level pavement crack detection using deep convolutional neural network with residual blocks

Road condition monitoring, such as surface defects and pavement cracks detection, is an important task in road management. Automated road surface defect detection is also a challenging problem in computer vision and machine learning research due to the large variety of pavement crack structures, variable lighting conditions, interfering objects on the road surface such as trashes, fallen tree leaves and branches. In this work, we develop a deep learning-based method for automated road surface defect and pavement crack detection. We design a deep convolutional neural network based on using residual blocks to predict the heatmaps which indicate the location and intensity of defects and cracks. To reduce false detection rates, we couple this heatmap prediction network with a binary classification network which is able to determine if the input image patch is normal or has defects. We test our method on the CFD benchmark dataset. Experiment results show that the proposed network is very effective for pavement crack detection and has more advanced performance than other methods.by Yu HouIncludes bibliographical reference

Road Deterioration detection A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis

Road surfaces may deteriorate over time because of a number of external factors such as heavy traffic, unfavourable weather, and poor design. These flaws, which may include potholes, fissures, and uneven surfaces, can pose significant safety threats to both vehicles and pedestrians. This research aims to develop and evaluate an automated system for detecting and analyzing cracks in pavements based on machine learning. The research explores the utilisation of object detection techniques to identify and categorize different types of pavement cracks. Additionally, the proposed work investigates several approaches to integrate the outcome system with existing pavement management systems to enhance road maintenance and sustainability. The research focuses on identifying reliable data sources, creating accurate and effective object detection algorithms for pavement crack detection, classifying various types of cracks, and assessing their severity and extent. The research objectives include gathering reliable datasets, developing a precise and effective object detection algorithm, classifying different types of pavement cracks, and determining the severity and extent of the cracks. The study collected pavement crack images from various sources, including publicly available databases and images captured using mobile devices. Multiple object detection models, such as YOLOv5, YOLOv8, and CenterNet were trained and tested using the collected dataset. The proposed approaches were evaluated using different performance metrics, The achieved results indicated that the YOLOv5 model outperformed CenterNet by a significant margin

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

Evaluación de daños en pavimento flexible usando fotogrametría terrestre y redes neuronales

In Colombia, road deterioration is assessed by means of road inventories and visual inspections. For this assessment, the Instituto Nacional de Vías (Colombia's National Road Institute) (abbreviated INVIAS in Spanish) uses the Vision Inspection de Zones et Itinéraires Á Risque (VIZIR) and Pavement Index Condition (PCI) methods. These two methods serve to determine the severity of damages in flexible and rigid pavements. However, they can be tedious and subjective and require an experienced evaluator, hence the need to develop new methods for road condition assessment. In this paper, we present a methodology to evaluate flexible pavement deterioration using terrestrial photogrammetry techniques and neural networks. The proposed methodology consists of six stages: (i) image capture, (ii) image preprocessing, (iii) segmentation via edge detection techniques, (iv) characteristic extraction, (v) classification using neural networks, and (vi) assessment of deteriorated areas. It is verified using real images of three different pavement distresses: longitudinal cracking, crocodile cracking, and pothole. As classifier, we use a multilayer neural network with a (12 12 3) configuration and trained using the Levenberg–Marquardt algorithm for backpropagation. The results show a classifier’s accuracy of 96 %, a sensitivity of 93.33 %, and a Cohen's Kappa coefficient of 93.67 %. Thus, our proposed methodology could pave the way for the development of an automated system to assess road deterioration, which may, in turn, reduce time and costs when designing road infrastructure maintenance plans.La evaluación del deterioro de las vías en Colombia se realiza por medio de inventarios manuales e inspecciones visuales. Los métodos de evaluación del estado de las vías adoptados por el INVIAS (Instituto Nacional de Vías) son VIZIR (Visión Inspection de Zones et Itinéraires Á Risque) y PCI (Paviment Condition Index). Estos determinan la gravedad de daño en pavimento flexible y rígido; sin embargo, pueden ser tediosos, subjetivos y requieren de la experiencia de un evaluador, lo que evidencia la necesidad de desarrollar metodologías de evaluación del estado de las vías. Este documento presenta una metodología para la evaluación de los deterioros presentes en pavimento flexible usando técnicas de fotogrametría terrestre y redes neuronales que está compuesta por seis etapas: i. Captura de las imágenes, ii. Preprocesamiento de las imágenes, iii. Segmentación mediante técnicas de detección de bordes, iv. Extracción de las características, v. Clasificación utilizando redes neuronales, y vi. Evaluación del área de afectación del deterioro. La metodología se evaluó con imágenes reales de pavimento con tres tipos de deterioro: grieta longitudinal, piel de cocodrilo y bache. Como clasificador se utilizó una red neuronal multicapa con configuración (12 12 3), entrenada utilizando el algoritmo Levenberg Marquardt de retropropagación. Se obtuvo una exactitud del 96 % en el clasificador, una sensibilidad de 93.33 % y una índice kappa de 0.936. Esta metodología es la base para la creación de un sistema automatizado de evaluación del deterioro presente en las vías, el cual puede contribuir en la reducción en tiempo y costo en los planes de gestión de mantenimiento de la infraestructura vial

Detection and Localization of Linear Features Based on Image Processing Methods

In this work, the general problem of the detection of features in images is considered. One of the methods, the orientation detection of lines, utilized the Radon transform (sinogram) of an image to detect lines at different angles in an image. The line thickness algorithm was generated by finding a pattern formed by particular lines in an image. The filtering of reconstructed images dealt with the removal of blur and other artifacts that arose in the course of inverting the Radon transform of an image to attempt to obtain the original image

Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture non-uniformities making their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough and expedited health monitoring of roads. In the pavement monitoring area, well known texture descriptors such as gray-level co-occurrence matrices and local binary patterns are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature

Multi-image-feature-based hierarchical concrete crack identification framework using optimized SVM multi-classifiers and D-S fusion algorithm for bridge structures

Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. On the basis of multiple image features, this work presents a novel approach for crack identification of concrete structures. Firstly, the non-local means method is adopted to process the original image, which can effectively diminish the noise influence. Then, to extract the effective features sensitive to the crack, different filters are employed for crack edge detection, which are subsequently tackled by integral projection and principal component analysis (PCA) for optimal feature selection. Moreover, support vector machine (SVM) is used to design the classifiers for initial diagnosis of concrete surface based on extracted features. To raise the classification accuracy, enhanced salp swarm algorithm (ESSA) is applied to the SVM for meta-parameter optimization. The Dempster–Shafer (D–S) fusion algorithm is utilized to fuse the diagnostic results corresponding to different filters for decision making. Finally, to demonstrate the effectiveness of the proposed framework, a total of 1200 images are collected from a real concrete bridge including intact (without crack), longitudinal crack, transverse crack and oblique crack cases. The results validate the performance of proposed method with promising results of diagnosis accuracy as high as 96.25%