Documents
Presentation Slides
Presentation Slides
Deep Learning-based Obstacle Detection and Depth Estimation
- Citation Author(s):
- Submitted by:
- Yi-Yu Hsieh
- Last updated:
- 19 September 2019 - 9:32pm
- Document Type:
- Presentation Slides
- Document Year:
- 2019
- Event:
- Presenters:
- Jen-Hui Chuang
- Paper Code:
- 3455
- Categories:
- Keywords:
- Log in to post comments
This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains. Experimental results show that the proposed model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset, while the unified approach can actually improve both (i) and (ii) at the same time.