Research on Automatic Driving Object Detection based on Point Cloud and Image Data Fusion

Jian Di ； Yedan Wang

《Frontiers in Science and Engineering》 1卷5期 (2021/08) Pp. 24-30

https://doi.org/10.29556/FSE.202108_1(5).0005

摘要

Researchers focus on using multiple sensors to improve the accuracy of object detection model in automatic driving. Therefore, the research on data fusion method in object detection has important academic and application value. Therefore, this paper expounds from three aspects: point cloud and image data fusion object detection, the level of data fusion and the calculation method of data fusion, and comprehensively shows the cutting-edge progress in this field; Finally, the challenges, strategies and prospects of integration practice are summarized.

關鍵字

Automatic Driving ； Point Cloud ； Image ； Fusion

參考文獻

Hariharan B, Arbeláez P, Girshick R, et al. Simultaneous detection and segmentation [C]// European conference on computer vision. Springer, Cham, 2014: 297-312.

Kang K, Li H, Yan J, et al. T-cnn: Tubelets with convolutional neural networks for object detection from videos [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 28(10): 2896-2907.

Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection [C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.

Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector [C]// European conference on computer vision. Springer, Cham, 2016: 21-37.

Meyer G P, Laddha A, Kee E, et al. Lasernet: An efficient probabilistic 3d object detector for autonomous driving [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 12677-12686.