Object detection in camera images, using deep learning has been proven successfully in
recent years. Rising detection rates and computationally efficient network structures are …

Object detection using remote sensing data is a key task of the perception systems of self-
driving vehicles. While many generic deep learning architectures have been proposed for …

Camera and radar sensors have significant advantages in cost, reliability, and maintenance
compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at …

Various autonomous or assisted driving strategies have been facilitated through the
accurate and reliable perception of the environment around a vehicle. Among the commonly …

Recent advances in camera equipped drone applications and their widespread use
increased the demand on vision based object detection algorithms for aerial images. Object …

Robust 3D object detection is critical for safe autonomous driving. Camera and radar
sensors are synergistic as they capture complementary information and work well under …

Radar is usually more robust than the camera in severe driving scenarios, eg, weak/strong
lighting and bad weather. However, unlike RGB images captured by a camera, the semantic …

Enhancing the accuracy and reliability of perception systems in automated vehicles is
critical, especially under varying driving conditions. Unfortunately, the challenges of adverse …

Robust and accurate scene representation is essential for advanced driver assistance
systems (ADAS) such as automated driving. The radar and camera are two widely used …

Abstract Objects captured by UAVs and drones in city scenes usually come in various sizes
and are extremely dense. Therefore, we propose a hybrid detector, called RRNet, for object …