Publications

Diverging from conventional heavy-fusion paradigms, the CORE-Net framework adopts a dual-branch architecture integrated with a streamlined Cross-modal Concatenation Network Framework (CCNF), which achieves efficient feature integration while substantially reducing model complexity.

This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.

This work proposes an improved, lightweight algorithm, LCFF-Net, designed to enhance the extraction of tiny target features and optimize the use of computational resources, and presents different scale versions of the LCFF-Net algorithm to suit various deployment environments.