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Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms
Computer vision algorithms have been utilized for 3-D road imaging and pothole detection
for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of …
for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of …
Automated road defect and anomaly detection for traffic safety: a systematic review
Recently, there has been a substantial increase in the development of sensor technology.
As enabling factors, computer vision (CV) combined with sensor technology have made …
As enabling factors, computer vision (CV) combined with sensor technology have made …
Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection
Freespace detection is an essential component of visual perception for self-driving cars. The
recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly …
recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly …
Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms
Joint detection of drivable areas and road anomalies is very important for mobile robots.
Recently, many semantic segmentation approaches based on convolutional neural …
Recently, many semantic segmentation approaches based on convolutional neural …
Rethinking road surface 3-D reconstruction and pothole detection: From perspective transformation to disparity map segmentation
Potholes are one of the most common forms of road damage, which can severely affect
driving comfort, road safety, and vehicle condition. Pothole detection is typically performed …
driving comfort, road safety, and vehicle condition. Pothole detection is typically performed …
RoadFormer: Duplex transformer for RGB-normal semantic road scene parsing
The recent advancements in deep convolutional neural networks have shown significant
promise in the domain of road scene parsing. Nevertheless, the existing works focus …
promise in the domain of road scene parsing. Nevertheless, the existing works focus …
PVStereo: Pyramid voting module for end-to-end self-supervised stereo matching
Supervised learning with deep convolutional neural networks (DCNNs) has seen huge
adoption in stereo matching. However, the acquisition of large-scale datasets with well …
adoption in stereo matching. However, the acquisition of large-scale datasets with well …
SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection
Freespace detection is a fundamental component of autonomous driving perception.
Recently, deep convolutional neural networks (DCNNs) have achieved impressive …
Recently, deep convolutional neural networks (DCNNs) have achieved impressive …
PotSpot: Participatory sensing based monitoring system for pothole detection using deep learning
Proper maintenance of roads is an extremely complex task and also an important issue all
over the world. One of the most critical road monitoring and maintenance activities is the …
over the world. One of the most critical road monitoring and maintenance activities is the …
Fast road segmentation via uncertainty-aware symmetric network
Y Chang, F Xue, F Sheng, W Liang… - … Conference on Robotics …, 2022 - ieeexplore.ieee.org
The high performance of RGB-D based road segmentation methods contrasts with their rare
application in commercial autonomous driving, which is owing to two reasons: 1) the prior …
application in commercial autonomous driving, which is owing to two reasons: 1) the prior …