A deep learning approach to crack detection on road surfaces

Currently, modern achievements in the field of deep learning are increasingly being applied in practice. One of the practical uses of deep learning is to detect cracks on the surface of the roadway. The destruction of the roadway is the result of various factors: for example, the use of low-quality material, non-compliance with the standards of laying asphalt, external physical impact, etc. Detection of these damages in automatic mode with high speed and accuracy is an important and complex task. An effective solution to this problem can reduce the time of services that carry out the detection of damage and also increase the safety of road users. The main challenge for automatically detecting such damage, in most cases, is the complex structure of the roadway. To accurately detect this damage, we use U-Net. After that we improve the binary map with localized cracks from the U-Net neural network, using the morphological filtering. This solution allows localizing cracks with higher accuracy in comparison with traditional methods crack detection, as well as modern methods of deep learning. All experiments were performed using the publicly available CRACK500 dataset with examples of cracks and their binary maps

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 The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy.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

A simplified computer vision system for road surface inspection and maintenance

This paper presents a computer vision system whose aim is to detect and classify cracks on road surfaces. Most of the previous works consisted of complex and expensive acquisition systems, whereas we have developed a simpler one composed by a single camera mounted on a light truck and no additional illumination. The system also includes tracking devices in order to geolocalize the captured images. The computer vision algorithm has three steps: hard shoulder detection, cell candidate proposal, and crack classification. First the region of interest (ROI) is delimited using the Hough transform (HT) to detect the hard shoulders. The cell candidate step is divided into two substeps: Hough transform features (HTF) and local binary pattern (LBP). Both of them split up the image into nonoverlapping small grid cells and also extract edge orientation and texture features, respectively. At the fusion stage, the detection is completed by mixing those techniques and obtaining the crack seeds. Afterward, their shape is improved using a new developed morphology operator. Finally, one classification based on the orientation of the detected lines has been applied following the Chain code. Massive experiments were performed on several stretches on a Spanish road showing very good performance

CHARACTERIZATION OF ENGINEERED SURFACES

In the recent years there has been an increasing interest in manufacturing products where surface topography plays a functional role. These surfaces are called engineered surfaces and are used in a variety of industries like semi conductor, data storage, micro- optics, MEMS etc. Engineered products are designed, manufactured and inspected to meet a variety of specifications such as size, position, geometry and surface finish to control the physical, chemical, optical and electrical properties of the surface. As the manufacturing industry strive towards shrinking form factor resulting in miniaturization of surface features, measurement of such micro and nanometer scale surfaces is becoming more challenging. Great strides have been made in the area of instrumentation to capture surface data, but the area of algorithms and procedures to determine form, size and orientation information of surface features still lacks the advancement needed to support the characterization requirements of R&D and high volume manufacturing. This dissertation addresses the development of fast and intelligent surface scanning algorithms and methodologies for engineered surfaces to determine form, size and orientation of significant surface features. Object recognition techniques are used to identify the surface features and CMM type fitting algorithms are applied to calculate the dimensions of the features. Recipes can be created to automate the characterization and process multiple features simultaneously. The developed methodologies are integrated into a surface analysis toolbox developed in MATLAB environment. The deployment of the developed application on the web is demonstrated

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

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

Automatic Main Road Extraction from High Resolution Satellite Imagery

Road information is essential for automatic GIS (geographical information system) data acquisition, transportation and urban planning. Automatic road (network) detection from high resolution satellite imagery will hold great potential for significant reduction of database development/updating cost and turnaround time. From so called low level feature detection to high level context supported grouping, so many algorithms and methodologies have been presented for this purpose. There is not any practical system that can fully automatically extract road network from space imagery for the purpose of automatic mapping. This paper presents the methodology of automatic main road detection from high resolution satellite IKONOS imagery. The strategies include multiresolution or image pyramid method, Gaussian blurring and the line finder using 1-dimemsional template correlation filter, line segment grouping and multi-layer result integration. Multi-layer or multi-resolution method for road extraction is a very effective strategy to save processing time and improve robustness. To realize the strategy, the original IKONOS image is compressed into different corresponding image resolution so that an image pyramid is generated; after that the line finder of 1-dimemsional template correlation filter after Gaussian blurring filtering is applied to detect the road centerline. Extracted centerline segments belong to or do not belong to roads. There are two ways to identify the attributes of the segments, the one is using segment grouping to form longer line segments and assign a possibility to the segment depending on the length and other geometric and photometric attribute of the segment, for example the longer segment means bigger possibility of being road. Perceptual-grouping based method is used for road segment linking by a possibility model that takes multi-information into account; here the clues existing in the gaps are considered. Another way to identify the segments is feature detection back-to-higher resolution layer from the image pyramid