Intelligent connected vehicles (ICVs) are believed to change people's life in the near future
by making the transportation safer, cleaner and more comfortable. Although many …

This paper explores deep learning (DL) methods that are used or have the potential to be
used for traffic video analysis, emphasising driving safety for both autonomous vehicles and …

Reliable traffic light detection and classification is crucial for automated driving in urban
environments. Currently, there are no systems that can reliably perceive traffic lights in real …

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles
to execute correct judgments. However, existing benchmarks tend to oversimplify the scene …

This paper presents the challenges that researchers must overcome in traffic light
recognition (TLR) research and provides an overview of ongoing work. The aim is to …

Traffic sign detection, as an important part of intelligent driving, can effectively guide drivers
to regulate driving and reduce the occurrence of traffic accidents. Currently, the deep …

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding
challenge in the field of computer vision. Specifically, object detection in drone images is a …

Recent improvements in object detection are driven by the success of convolutional neural
networks (CNN). They are able to learn rich features outperforming hand-crafted features …

The accurate detection and recognition of traffic lights is important for autonomous vehicle
navigation and advanced driver aid systems. In this paper, we present a traffic light …

Reliable real-time detection of traffic lights is a major concern for the task of autonomous
driving. As deep convolutional networks have proven to be a powerful tool in visual object …