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

Multiple vehicle fusion for a robust road condition estimation based on vehicle sensors and data mining

The road condition is an important factor for driving comfort and has impact on safety, economy and health. Delayed detection of defects lead to renewals which yields to complete roadblocks or traffic jams. Therefore, an early identification of road defects is desirable. Novel condition monitoring systems employ vehicles as sensor platforms and apply machine learning methods to predict the road condition based on the sensor data. The paper addresses the question how to combine the classification results from different vehicles to improve the final prediction. Different fusion strategies are investigated in various scenarios in a novel simulation. It is demonstrated that the performance of the classification can be improved compared to a majority vote or only considering one vehicle by taking the probability for the prediction of each vehicle into account. The probabilities follow a multinomial distribution and the precision matrix of the classifiers provide the best parameters. Overall, the results show that the application of the presented fusion strategies on road condition estimation greatly improve the performance and guarantee a robust detection of defects

Multiple vehicle fusion for a robust road condition estimation based on vehicle sensors and data mining

The road condition is an important factor for driving comfort and has impact on safety, economy and health. Delayed detection of defects lead to renewals which yields to complete roadblocks or traffic jams. Therefore, an early identification of road defects is desirable. Novel condition monitoring systems employ vehicles as sensor platforms and apply machine learning methods to predict the road condition based on the sensor data. The paper addresses the question how to combine the classification results from different vehicles to improve the final prediction. Different fusion strategies are investigated in various scenarios in a novel simulation. It is demonstrated that the performance of the classification can be improved compared to a majority vote or only considering one vehicle by taking the probability for the prediction of each vehicle into account. The probabilities follow a multinomial distribution and the precision matrix of the classifiers provide the best parameters. Overall, the results show that the application of the presented fusion strategies on road condition estimation greatly improve the performance and guarantee a robust detection of defects

INTELLIGENT ROAD MAINTENANCE: A MACHINE LEARNING APPROACH FOR SURFACE DEFECT DETECTION

The emergence of increased sources for Big Data through consumer recording devices gives rise to a new basis for the management and governance of public infrastructures and policy de-sign. Road maintenance and detection of road surface defects, such as cracks, have traditionally been a time consuming and manual process. Lately, increased automation using easily acquirable front-view digital natural scene images is seen to be an alternative for taking timely maintenance decisions; reducing accidents and operating cost and increasing public safety. In this paper, we propose a machine learning based approach to handle the challenge of crack and related defect detection on road surfaces using front-view images captured from driver’s viewpoint under diverse conditions. We use a superpixel based method to first process the road images into smaller coherent image regions. These superpixels are then classified into crack and non-crack regions. Various texture-based features are combined for the classification mod-el. Classifiers such as Gradient Boosting, Artificial Neural Network, Random Forest and Linear Support Vector Machines are evaluated for the task. Evaluations on real datasets show that the approach successfully handles different road surface conditions and crack-types, while locating the defective regions in the scene images

Deep Learning for Crack-Like Object Detection

Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a huge cost to maintain and upgrade such an immense road network. Thus, fully automatic crack detection has received increasing attention. With the development of artificial intelligence (AI), the deep learning technique has achieved great success and has been viewed as the most promising way for crack detection. Based on deep learning, this research has solved four important issues existing in crack-like object detection. First, the noise problem caused by the textured background is solved by using a deep classification network to remove the non-crack region before conducting crack detection. Second, the computational efficiency is highly improved. Third, the crack localization accuracy is improved. Fourth, the proposed model is very stable and can be used to deal with a wide range of crack detection tasks. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human’s intervention

Evaluation of radiography as a screening method for detection and characterisation of congenital vertebral malformations in dogs

Congenital vertebral malformations (CVM) are common in brachycephalic ‘screw-tailed’ dogs; they can be associated with neurological deficits and a genetic predisposition has been suggested. The purpose of this study was to evaluate radiography as a screening method for congenital thoracic vertebral malformations in brachycephalic ‘screw-tailed’ dogs by comparing it with CT. Forty-nine dogs that had both radiographic and CT evaluations of the thoracic vertebral column were included. Three observers retrospectively reviewed the images independently to detect CVMs. When identified, they were classified according to a previously published radiographic classification scheme. A CT consensus was then reached. All observers identified significantly more affected vertebrae when evaluating orthogonal radiographic views compared with lateral views alone; and more affected vertebrae with the CT consensus compared with orthogonal radiographic views. Given the high number of CVMs per dog, the number of dogs classified as being CVM free was not significantly different between CT and radiography. Significantly more midline closure defects were also identified with CT compared with radiography. Malformations classified as symmetrical or ventral hypoplasias on radiography were frequently classified as ventral and medial aplasias on CT images. Our results support that CT is better than radiography for the classification of CVMs and this will be important when further evidence of which are the most clinically relevant CVMs is identified. These findings are of particular importance for designing screening schemes of CVMs that could help selective breeding programmes based on phenotype and future studies

Correct and Control Complex IoT Systems: Evaluation of a Classification for System Anomalies

In practice there are deficiencies in precise interteam communications about system anomalies to perform troubleshooting and postmortem analysis along different teams operating complex IoT systems. We evaluate the quality in use of an adaptation of IEEE Std. 1044-2009 with the objective to differentiate the handling of fault detection and fault reaction from handling of defect and its options for defect correction. We extended the scope of IEEE Std. 1044-2009 from anomalies related to software only to anomalies related to complex IoT systems. To evaluate the quality in use of our classification a study was conducted at Robert Bosch GmbH. We applied our adaptation to a postmortem analysis of an IoT solution and evaluated the quality in use by conducting interviews with three stakeholders. Our adaptation was effectively applied and interteam communications as well as iterative and inductive learning for product improvement were enhanced. Further training and practice are required.Comment: Submitted to QRS 2020 (IEEE Conference on Software Quality, Reliability and Security

Hodnotenie kvality povrchu vozovky v cestnom tuneli

The paper discusses a method for quantitative evaluation of the road surface based on processing of the cloud of 3D points. The points are measured by high-speed laser scanner mounted on the vehicle. Such approach allows to scan the road surface without the need to stop the traffic inside the tunnel. The obtained data are processed in offline mode. The processing algorithm evaluates the structure (texture) of the surface, which has the direct impact on the safety of the road transport.Článok sa zaoberá metódou kvalitatívneho hodnotenia povrchu vozovky, založenou na spracovaní mračna 3D bodov. Body sú získané meraním pomocou vysokorýchlostného laserového skenera namontovaného priamo na vozidle. Tento prístup umožňuje skenovanie povrchu vozovky bez nutnosti zastaviť premávku v tuneli. Získané dáta sú spracovávané off-line. Algoritmus spracovania dát hodnotí štruktúru (textúru) povrchu vozovky, ktorá má priamy dopad na bezpečnosť prevádzky na cestnej komunikácii