380 research outputs found
Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey
Ensuring traffic safety is crucial, which necessitates the detection and
prevention of road surface defects. As a result, there has been a growing
interest in the literature on the subject, leading to the development of
various road surface defect detection methods. The methods for detecting road
defects can be categorised in various ways depending on the input data types or
training methodologies. The predominant approach involves image-based methods,
which analyse pixel intensities and surface textures to identify defects.
Despite their popularity, image-based methods share the distinct limitation of
vulnerability to weather and lighting changes. To address this issue,
researchers have explored the use of additional sensors, such as laser scanners
or LiDARs, providing explicit depth information to enable the detection of
defects in terms of scale and volume. However, the exploration of data beyond
images has not been sufficiently investigated. In this survey paper, we provide
a comprehensive review of road surface defect detection studies, categorising
them based on input data types and methodologies used. Additionally, we review
recently proposed non-image-based methods and discuss several challenges and
open problems associated with these techniques.Comment: Survey paper
Multi-Scale Attention Networks for Pavement Defect Detection
Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multi-scale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder-decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. Instead of the original 3×3 convolution, the multi-scale convolution kernels are utilized in depth-wise separable convolution layers of the network. Further, the hybrid attention mechanism is separately incorporated into the encoder and decoder modules to infer the significance of spatial points and inter-channel relationship features for the input intermediate feature maps. The proposed approach achieves state-of-the-art performance on two publicly-available benchmark datasets, i.e., the Crack500 (500 crack images with 2,000×1,500 pixels) and CFD (118 crack images with 480×320 pixels) datasets. The mean intersection over union ( MIoU ) of the proposed approach on these two datasets reaches 0.7219 and 0.7788, respectively. Ablation experiments show that the multi-scale convolution and hybrid attention modules can effectively help the model extract high-level feature representations and generate more accurate pavement crack segmentation results. We further test the model on locally collected pavement crack images (131 images with 1024×768 pixels) and it achieves a satisfactory result. The proposed approach realizes the MIoU of 0.6514 on the local dataset and outperforms other compared baseline methods. Experimental findings demonstrate the validity and feasibility of the proposed approach and it provides a viable solution for pavement crack detection in practical application scenarios. Our code is available at https://github.com/xtu502/pavement-defects
Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies
This paper presents an accurate and stable method for object and defect detection and visualization on building and infrastructural facilities. This method uses drones and cameras to collect three- dimensional (3D) point clouds via photogrammetry, and uses orthographic or arbitrary views of the target objects to generate the feature images of points’ spectral, elevation, and normal features. U-Net is implemented in the pixelwise segmentation for object and defect detection using multiple feature images. This method was validated on four applications, including on-site path detection, pavement cracking detection, highway slope detection, and building facade window detection. The comparative experimental results confirmed that U-Net with multiple features has a better pixelwise segmentation performance than separately using each single feature. The developed method can implement object and defect detection with different shapes, including striped objects, thin objects, recurring and regularly shaped objects, and bulky objects, which will improve the accuracy and efficiency of inspection, assessment, and management of buildings and infrastructural facilities
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
Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios
Runway and taxiway pavements are exposed to high stress during their
projected lifetime, which inevitably leads to a decrease in their condition
over time. To make sure airport pavement condition ensure uninterrupted and
resilient operations, it is of utmost importance to monitor their condition and
conduct regular inspections. UAV-based inspection is recently gaining
importance due to its wide range monitoring capabilities and reduced cost. In
this work, we propose a vision-based approach to automatically identify
pavement distress using images captured by UAVs. The proposed method is based
on Deep Learning (DL) to segment defects in the image. The DL architecture
leverages the low computational capacities of embedded systems in UAVs by using
an optimised implementation of EfficientNet feature extraction and Feature
Pyramid Network segmentation. To deal with the lack of annotated data for
training we have developed a synthetic dataset generation methodology to extend
available distress datasets. We demonstrate that the use of a mixed dataset
composed of synthetic and real training images yields better results when
testing the training models in real application scenarios.Comment: 12 pages, 6 figures, published in proceedings of 15th International
Conference on Machine Vision (ICMV
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing
Road pavement detection and segmentation are critical for developing
autonomous road repair systems. However, developing an instance segmentation
method that simultaneously performs multi-class defect detection and
segmentation is challenging due to the textural simplicity of road pavement
image, the diversity of defect geometries, and the morphological ambiguity
between classes. We propose a novel end-to-end method for multi-class road
defect detection and segmentation. The proposed method comprises multiple
spatial and channel-wise attention blocks available to learn global
representations across spatial and channel-wise dimensions. Through these
attention blocks, more globally generalised representations of morphological
information (spatial characteristics) of road defects and colour and depth
information of images can be learned. To demonstrate the effectiveness of our
framework, we conducted various ablation studies and comparisons with prior
methods on a newly collected dataset annotated with nine road defect classes.
The experiments show that our proposed method outperforms existing
state-of-the-art methods for multi-class road defect detection and segmentation
methods.Comment: Accepted to the ICRA 202
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
An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment.
Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region\u27s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera\u27s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification ( presence/absence detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating
What's cracking? A review and analysis of deep learning methods for structural crack segmentation, detection and quantification
Surface cracks are a very common indicator of potential structural faults.
Their early detection and monitoring is an important factor in structural
health monitoring. Left untreated, they can grow in size over time and require
expensive repairs or maintenance. With recent advances in computer vision and
deep learning algorithms, the automatic detection and segmentation of cracks
for this monitoring process have become a major topic of interest. This review
aims to give researchers an overview of the published work within the field of
crack analysis algorithms that make use of deep learning. It outlines the
various tasks that are solved through applying computer vision algorithms to
surface cracks in a structural health monitoring setting and also provides
in-depth reviews of recent fully, semi and unsupervised approaches that perform
crack classification, detection, segmentation and quantification. Additionally,
this review also highlights popular datasets used for cracks and the metrics
that are used to evaluate the performance of those algorithms. Finally,
potential research gaps are outlined and further research directions are
provided
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