@Article{jai.2026.075753,
AUTHOR = {Jianping Wu, Nian Li, Libin Dong, Ping Zhang},
TITLE = {Camera-LiDAR Fusion for Enhanced Object Detection},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {8},
YEAR = {2026},
NUMBER = {1},
PAGES = {259--271},
URL = {http://www.techscience.com/jai/v8n1/67352},
ISSN = {2579-003X},
ABSTRACT = {This paper presents a static fusion framework that enhances object detection by integrating camera and LiDAR-based detection results. The proposed method focuses on associating 2D candidate bounding boxes from a camera detector with 3D candidate boxes from a LiDAR detector using an Intersection over Union (IoU)-based matching approach. To enhance the quality of 2D detection, we refine the baseline Cascade R-CNN detector by incorporating a dual self-attention mechanism into both the backbone and the region proposal network (RPN), resulting in the DA-Cascade R-CNN. This enhancement strengthens the network’s ability to detect small or distant objects by improving feature sensitivity and localization accuracy. Once 2D and 3D candidate boxes are obtained, they are associated through IoU-aware matching and subsequently refined using non-maximum suppression (NMS) to remove redundant or conflicting hypotheses across modalities, effectively preserving positive detection results to improve accuracy. Experimental results on the KITTI dataset demonstrate that the proposed static fusion method yields improved detection average precision for three different levels of difficulty compared to single-sensor baselines.},
DOI = {10.32604/jai.2026.075753}
}