Multi-modal fusion is imperative to the implementation of reliable object detection and
tracking in complex environments. Exploiting the synergy of heterogeneous modal …

Digital construction relies on effective sensing to enhance the safety, productivity, and
quality of its activities. However, current sensing devices (eg, camera, LiDAR, infrared …

Accurate and robust object detection is critical for autonomous driving. Image-based
detectors face difficulties caused by low visibility in adverse weather conditions. Thus, radar …

Due to millimeter-wave (MMW) radar's ability to directly acquire spatial positions and velocity
information of objects, as well as its robust performance in adverse weather conditions, it …

Recently, object detection methods based on multi-modal fusion have gained widespread
adoption in autonomous driving, proving to be valuable for detecting objects in dynamic …

In real-world object detection applications, the camera would be affected by poor lighting
conditions, resulting in a deteriorate performance. Millimeter-wave radar and camera have …

Preventing car collisions through object detection has always been a major research
direction in the field of autonomous driving. Recent years, camera-based object detection …

When it comes to road environment perception, millimeter-wave radar with a camera
facilitates more reliable detection than a single sensor. However, the limited utilization of …

Employing deep learning methodologies for computer vision tasks, particularly in the
domain of radar image analysis, necessitates access to a large and diverse dataset. In the …