Created by Feng Xue
Official Matlab Implementation for "A Novel Multi-layer Framework for Tiny Obstacle Discovery", ICRA 2019
This repository contains the official Matlab implementation for "A Novel Multi-layer Framework for Tiny Obstacle Discovery". This paper has been accepted by IEEE International Conference on Robotics and Automation (ICRA) 2019. And the official Python/ROS implementation is coming soon.
Note: This version has been slightly improved on the basis of the original work, mainly including the following points:
- The training code is slightly changed, ROC performance is improved.
- For higher efficiency, the basic edge detection algorithm uses structured edge detection [1].
- The modules in the system are largely accelerated, although there is still a lot of room for improvement.
- To be consistent with other works in the community, in the instance-level evaluation, the IoU is defined as the Intersection over Union between predicted proposals and the ground truth bounding boxes, which can be found at
./evaluation/Func_evaluation_DR.m
If you find the paper or the code useful, please cite our paper:
@INPROCEEDINGS{Xue_ICRA_2019,
Author = {Feng Xue, Anlong Ming, Menghan Zhou and Yu Zhou},
booktitle={2019 International Conference on Robotics and Automation (ICRA)},
title={A Novel Multi-layer Framework for Tiny Obstacle Discovery},
year={2019},
}
You may download the original images from here and annotations from here. Then, you will have the following directory structure:
lostandfound
|_ leftImg8bit
| |_ test
| | |_ 02_Hanns_Klemm_Str_44
| | |_ ...
| | |_ 15_Rechbergstr_Deckenpfronn
| |_ train
| | |_ 01_Hanns_Klemm_Str_45
| | |_ ...
| | |_ 14_Otto_Lilienthal_Str_24
|_ gtCoarse
| |_ test
| | |_ 02_Hanns_Klemm_Str_44
| | |_ ...
| | |_ 15_Rechbergstr_Deckenpfronn
| |_ train
| | |_ 01_Hanns_Klemm_Str_45
| | |_ ...
| | |_ 14_Otto_Lilienthal_Str_24
For testing the algorithm on lost and found dataset, you can run the script in MATLAB:
./test.m
For Training the algorithm on lost and found dataset, you can run the script in MATLAB:
./train.m
The evaluations are conducted at the end of test.m
. And there are three scripts for evaluation:
./evaluation/Func_evaluation_DR.m
./evaluation/Func_evaluation_ROC.m
./evaluation/Func_evaluation_time.m
For visualization, to run the script:
./result/figuredraw.m
The performances are presented as follows:
[1] Dollar P , Zitnick C L . Fast Edge Detection Using Structured Forests[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(8):1558-1570.
[2] Zitnick C L , Dollar P . Edge Boxes: Locating Object Proposals from Edges[C]// European Conference on Computer Vision. Springer, Cham, 2014.
[3] Ma J , Ming A , Huang Z , et al. Object-Level Proposals[C]// IEEE International Conference on Computer Vision. IEEE, 2017.
[4] Pinggera P , Ramos S , Gehrig S , et al. Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016.
2019/12/24, we are applying for the license. The code will be released after we obtain the license.
2020/07/04, the Matlab implementation is released.