# Expandable YOLO: 3D Object Detection from RGB-D Images

arXiv: https://arxiv.org/abs/2006.14837 · HTML: https://mlsanity.com/paper/2006.14837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data](https://mlsanity.com/paper/2406.14581/) (2024)
- [RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing](https://mlsanity.com/paper/2212.01098/) (2022)

## Abstract (arXiv)

This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. Specifically, by extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction. In addition, Intersection over Uninon (IoU) in 3D space is introduced to confirm the accuracy of region extraction results. In the field of deep learning, object detectors that use distance information as input are actively studied for utilizing automated driving. However, the conventional detector has a large network structure, and the real-time property is impaired. The effectiveness of the detector constructed as described above is verified using datasets. As a result of this experiment, the proposed model is able to output 3D bounding boxes and detect people whose part of the body is hidden. Further, the processing speed of the model is 44.35 fps.