Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has
emerged as a crucial aspect of 3D object detection in autonomous driving. However …

With the growing availability of extensive 3D datasets and the rapid progress in
computational power, deep learning (DL) has emerged as a highly promising approach for …

Accurate, fast and lightweight dense target detection methods are highly important for
precision agriculture. To detect dense apricot flowers using drones, we propose an …

LiDAR-based sparse 3-D object detection plays a crucial role in autonomous driving
applications due to its computational efficiency advantages. Existing methods either use the …

This paper provides an in-depth review of deep learning techniques to address the
challenges of odometry and global ego-localization using frequency modulated continuous …

Fusing multi-modality inputs from different sensors is an effective way to improve the
performance of 3D object detection. However, current methods overlook two important …

Multimodal object detection leverages diverse modal information to enhance the accuracy
and robustness of detectors. Due to its ability to capture long-range dependencies, the …

Currently, multi-modal 3D object detection methods have become a key area of research in
the field of autonomous driving. Fusion is an essential factor affecting performance in multi …

At present, the top-down approach requires the introduction of pedestrian detection
algorithms in multi-person pose estimation. In this paper, we propose an end-to-end …

Long-range dependency plays a critical role in extracting intricate image features
particularly in tasks involving image recognition. In previous study, the significance of long …