This paper provides a comprehensive review of the YOLO (You Only Look Once) framework
up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has …

Feature-level fusion methods have demonstrated superior performance for visible-infrared
object detection due to the deep exploration of visible and infrared features. However, most …

Despite the success of deep learning-based object detection methods in recent years, it is
still challenging to make the object detector reliable in adverse weather conditions such as …

Recognition of surrounding objects is crucial for ensuring the safety of automated driving
systems. In the realm of 3D object recognition through deep learning, several methods …

The detection of vehicles, pedestrians, and obstacles plays an important role in the decision-
making process of autonomous vehicles. While existing methods achieve high detection …

The effectiveness of object detection is significantly hampered in challenging nighttime or
rainy scenarios. This is due to the severe domain shifts between daytime and adverse-visual …

Existing object detectors encounter challenges in handling domain shifts between training
and real-world data, particularly under poor visibility conditions like fog and night. Cutting …

Adverse weather conditions pose a significant challenge to the widespread adoption of
Autonomous Vehicles (AVs) by impacting sensors like LiDARs and cameras. Even though …

This work presents a method for monitoring the quality of the data captured by the cameras
of an Advanced Driver Assistance System or an Autonomous Driving vehicle. Adverse …

Autonomous vehicle technologies have become increasingly popular over the last few
years. One of their most important application is autonomous shuttle buses that could …