Road condition monitoring using smart sensing and artificial intelligence: A review
Road condition monitoring (RCM) has been a demanding strategic research area in
maintaining a large network of transport infrastructures. With advancements in computer …
maintaining a large network of transport infrastructures. With advancements in computer …
[HTML][HTML] A review on pavement distress and structural defects detection and quantification technologies using imaging approaches
C Chu, L Wang, H Xiong - Journal of Traffic and Transportation …, 2022 - Elsevier
Pavement distress detection (PDD) plays a vital role in planning timely pavement
maintenance that improves pavement service life. In order to promote the development of …
maintenance that improves pavement service life. In order to promote the development of …
A hybrid deep learning pavement crack semantic segmentation
Automatic pavement crack segmentation plays a critical role in the field of defect inspection.
Although recent segmentation-based CNNs studies showed a promising pavement crack …
Although recent segmentation-based CNNs studies showed a promising pavement crack …
Deep learning-based thermal image analysis for pavement defect detection and classification considering complex pavement conditions
Automatic damage detection using deep learning warrants an extensive data source that
captures complex pavement conditions. This paper proposes a thermal-RGB fusion image …
captures complex pavement conditions. This paper proposes a thermal-RGB fusion image …
Weakly supervised pavement crack semantic segmentation based on multi-scale object localization and incremental annotation refinement
Automatic and accurate pavement crack detection is essential for cost-effective road
maintenance. Deep convolutional neural networks (DCNNs) are widely used in recent …
maintenance. Deep convolutional neural networks (DCNNs) are widely used in recent …
FSRDD: An efficient few-shot detector for rare city road damage detection
Road damage detection (RDD) is indispensable for safe autonomous driving. Existing RDD
models focus on designing feature representations following expert knowledge. However …
models focus on designing feature representations following expert knowledge. However …
TransCrack: revisiting fine-grained road crack detection with a transformer design
Prior convolution-based road crack detectors typically learn more abstract visual
representation with increasing receptive field via an encoder–decoder architecture. Despite …
representation with increasing receptive field via an encoder–decoder architecture. Despite …
Automated detection of airfield pavement damages: an efficient light-weight algorithm
H Liang, H Gong, L Cong, M Zhang, Z Tao… - … Journal of Pavement …, 2023 - Taylor & Francis
Fast and accurate detection of airfield pavement damage is crucial to airport flight safety and
airfield pavement maintenance. An efficient and lightweight detection algorithm that can be …
airfield pavement maintenance. An efficient and lightweight detection algorithm that can be …
Self-supervised adversarial learning for domain adaptation of pavement distress classification
Pavement distress classification is crucial for the maintenance of highways. Although many
methods for classifying pavement distress are available, they all assume that training and …
methods for classifying pavement distress are available, they all assume that training and …
Deep domain adaptation for pavement crack detection
Deep learning-based pavement cracks detection methods often require large-scale labels
with detailed crack location information to learn accurate predictions. In practice, however …
with detailed crack location information to learn accurate predictions. In practice, however …