314 research outputs found
Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges
As a common appearance defect of concrete bridges, cracks are important
indices for bridge structure health assessment. Although there has been much
research on crack identification, research on the evolution mechanism of bridge
cracks is still far from practical applications. In this paper, the
state-of-the-art research on intelligent theories and methodologies for
intelligent feature extraction, data fusion and crack detection based on
data-driven approaches is comprehensively reviewed. The research is discussed
from three aspects: the feature extraction level of the multimodal parameters
of bridge cracks, the description level and the diagnosis level of the bridge
crack damage states. We focus on previous research concerning the quantitative
characterization problems of multimodal parameters of bridge cracks and their
implementation in crack identification, while highlighting some of their major
drawbacks. In addition, the current challenges and potential future research
directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to
author
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
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
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 3Ă—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 2,000Ă—1,500 pixels) and CFD (118 crack images with 480Ă—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 1024Ă—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
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
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
Optimized deep encoder-decoder methods for crack segmentation
Continuous maintenance of concrete infrastructure is an important task which
is needed to continue safe operations of these structures. One kind of defect
that occurs on surfaces in these structures are cracks. Automatic detection of
those cracks poses a challenging computer vision task as background, shape,
colour and size of cracks vary. In this work we propose optimized deep
encoder-decoder methods consisting of a combination of techniques which yield
an increase in crack segmentation performance. Specifically, we propose a new
design for the decoder-part in encoder-decoder based deep learning
architectures for semantic segmentation. We study its composition and how to
achieve increased performance by exploring components such as deep supervision
and upsampling strategies. Then we examine the optimal encoder to go in
conjunction with this decoder and determine that pretrained encoders lead to an
increase in performance. We propose a data augmentation strategy to increase
the amount of available training data and carry out the performance evaluation
of the designed architecture on four publicly available crack segmentation
datasets. Additionally, we introduce two techniques into the field of surface
crack segmentation, previously not used there: Generating results using
test-time-augmentation and performing a statistical result analysis over
multiple training runs. The former approach generally yields increased
performance results, whereas the latter allows for more reproducible and better
representability of a methods results. Using those aforementioned strategies
with our proposed encoder-decoder architecture we are able to achieve new state
of the art results in all datasets
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild
Automatic image-based pavement distress detection and recognition are vital
for pavement maintenance and management. However, existing deep learning-based
methods largely omit the specific characteristics of pavement images, such as
high image resolution and low distress area ratio, and are not end-to-end
trainable. In this paper, we present a series of simple yet effective
end-to-end deep learning approaches named Weakly Supervised Patch Label
Inference Networks (WSPLIN) for efficiently addressing these tasks under
various application settings. To fully exploit the resolution and scale
information, WSPLIN first divides the pavement image under different scales
into patches with different collection strategies and then employs a Patch
Label Inference Network (PLIN) to infer the labels of these patches. Notably,
we design a patch label sparsity constraint based on the prior knowledge of
distress distribution, and leverage the Comprehensive Decision Network (CDN) to
guide the training of PLIN in a weakly supervised way. Therefore, the patch
labels produced by PLIN provide interpretable intermediate information, such as
the rough location and the type of distress. We evaluate our method on a
large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive
results demonstrate the superiority of our method over baselines in both
performance and efficiency.Comment: Extension of ICASSP 2021 Paper entitled "Weakly Supervised Patch
Label Inference Network with Image Pyramid for Pavement Diseases Recognition
in the Wild", Submitted to IEEE T-IT
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 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
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
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