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 …

Joint detection of drivable areas and road anomalies is very important for mobile robots.
Recently, many semantic segmentation approaches based on convolutional neural …

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 …

Freespace detection is a fundamental component of autonomous driving perception.
Recently, deep convolutional neural networks (DCNNs) have achieved impressive …

Supervised learning with deep convolutional neural networks (DCNNs) has seen huge
adoption in stereo matching. However, the acquisition of large-scale datasets with well …

Recently, deep-learning based approaches have achieved impressive performance for
autonomous driving. However, end-to-end vision-based methods typically have limited …

Humans have long fantasized about self-driving vehicles for the sake of luxury, style, safety,
and ease. Free road space detection for collision avoidance and path planning is a vital part …

In this work, we present an alternative to conventional residual connections, which is
inspired by maxout nets. This means that instead of the addition in residual connections, our …

With the ongoing evolution of autonomous driving technology, road environment perception
systems have become a significant focus of research. However, there is currently a paucity …

Deep learning methods, especially convolutional neural networks (CNNs), are widely used
for industrial surface defect segmentation due to their excellent performance on visual …