Akademik Veri Yönetim Sistemi
25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, Macau, Çin, 8 - 12 Ekim 2022, cilt.2022-October, ss.2080-2085
© 2022 IEEE.This study presents an asymmetric late fusion approach to fuse camera and lidar outputs from different neural networks. By fusing two different modalities, each network's false positive results are eliminated and a superior vector space compared to single modality methods is created. Proposed 3D bounding boxes from lidar networks are supported with 2D segmentation networks that work with a camera. Ground plane provided by the lidar input is also detected to eliminate possible 2D camera drivable area false positive results, namely, roadblocks, grass, and side of tunnel walls. 3 camera networks and 6 different 3D lidar networks tested with KITTI dataset illustrate that the presented fusion approach exceeds the performance of Lidar-only methods up to 9.87% category wise. Comparison with two different fusion approaches also show our superior performance.