In recent years, the field of intelligent transportation has witnessed rapid advancements,
driven by the increasing demand for automation and efficiency in transportation systems …

Road network extraction from remotely sensed imagery has become a powerful tool for
updating geospatial databases, owing to the success of convolutional neural network (CNN) …

Semantic segmentation is a well-studied topic and one of the most challenging tasks in
computer vision applications, such as autonomous driving. Deep learning approaches …

Intelligent transportation systems (ITS) are among the most focused research in this century.
Actually, autonomous driving provides very advanced tasks in terms of road safety …

Due to deep learning's accurate cognition of the street environment, the convolutional neural
network has achieved dramatic development in the application of street scenes. Considering …

Nowadays, although conditional convolutional neural networks have applied to semantic
segmentation, their loss function needs to be carefully designed. We propose an improved …

Advanced Driver Assistance Systems (ADAS) consists of two basic functions. One is the
object detection for preventing vehicles from hitting pedestrians or other obstacles. The other …

Aiming at the problem that the existing semantic segmentation algorithms have a large
number of model parameters and are difficult to be used on mobile devices, a lightweight …

Semantic segmentation plays a pivotal role in enhancing the perception capabilities of
autonomous vehicles and self-driving cars, enabling them to comprehend and navigate …

Road scene semantic segmentation often requires a deeper neural network to obtain higher
accuracy, which makes the segmentation model more complex and slower. In this paper, we …