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 …
Pavement defect detection with deep learning: A comprehensive survey
Pavement defect detection is of profound significance regarding road safety, so it has been a
trending research topic. In the past years, deep learning based methods have turned into a …
trending research topic. In the past years, deep learning based methods have turned into a …
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 …
Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms
Existing road pothole detection approaches can be classified as computer vision-based or
machine learning-based. The former approaches typically employ 2D image analysis …
machine learning-based. The former approaches typically employ 2D image analysis …
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 …
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 …
Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation
Collision-free space detection is a critical component of autonomous vehicle perception. The
state-of-the-art algorithms are typically based on supervised deep learning. Their …
state-of-the-art algorithms are typically based on supervised deep learning. Their …
We learn better road pothole detection: from attention aggregation to adversarial domain adaptation
Manual visual inspection performed by certified inspectors is still the main form of road
pothole detection. This process is, however, not only tedious, time-consuming and costly, but …
pothole detection. This process is, however, not only tedious, time-consuming and costly, but …
SHREC 2022: Pothole and crack detection in the road pavement using images and RGB-D data
This paper describes the methods submitted for evaluation to the SHREC 2022 track on
pothole and crack detection in the road pavement. A total of 7 different runs for the semantic …
pothole and crack detection in the road pavement. A total of 7 different runs for the semantic …
Multi-scale feature fusion: Learning better semantic segmentation for road pothole detection
This paper presents a novel pothole detection approach based on single-modal semantic
segmentation. It first extracts visual features from input images using a convolutional neural …
segmentation. It first extracts visual features from input images using a convolutional neural …