Refereed article in conference proceedings (A4)

A Comparative Study of Deep Learning-based RGB-depth Fusion Methods for Object Detection




List of AuthorsFarahnakian Fahimeh, Heikkonen Jukka

EditorsM. Arif Wani, Feng Luo, Xiaolin (Andy) Li, Dejing Dou, Francesco Bonchi

Conference nameInternational Conference on Machine Learning and Applications

Publication year2021

Book title *2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

Start page1475

End page1482

ISBN978-1-7281-8471-5

eISBN978-1-7281-8470-8

DOIhttp://dx.doi.org/10.1109/ICMLA51294.2020.00228


Abstract

The object detection task which attempts to predict bounding boxes of all interest objects in an RGB image is of paramount importance for many real-world applications and has attracted much attention within the computer vision com- munity. However, RBG cameras cannot directly provide depth information and RGB-based object detector can not achieve an accurate performance under complex environment. To address this problem, we make two contributions in this paper. Firstly, the performances of four state-of-the art unsupervised depth estimation methods were thoroughly evaluated in the context of object detection, which can serve as a baseline for other researchers to develop even more sophisticated methods. Sec- ondly, we investigated whether fusing depth information and RGB can improve the performance of object detection networks. The obtained results on the KITTI dataset show that RGB-depth fusion approach with MonoDepth as depth estimation method outperforms the RGB-based and depth-based detectors.


Last updated on 2021-03-12 at 13:13