# Tiny-Obstacle-Discovery

**Repository Path**: mrobotit/Tiny-Obstacle-Discovery

## Basic Information

- **Project Name**: Tiny-Obstacle-Discovery
- **Description**: Official Matlab Implementation for "A Novel Multi-layer Framework for Tiny Obstacle Discovery", ICRA 2019
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-25
- **Last Updated**: 2021-04-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Tiny-Obstacle-Discovery

Created by Feng Xue

Official Matlab Implementation for "[A Novel Multi-layer Framework for Tiny Obstacle Discovery](https://arxiv.org/abs/1904.10161v3)", ICRA 2019


## Introduction

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```



## Citation

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},
}
```

## Data Preparation
### Lost and Found Dataset (LAF) [4]

You may download the original images from [here](http://www.dhbw-stuttgart.de/~sgehrig/lostAndFoundDataset/leftImg8bit.zip) and annotations from [here](http://www.dhbw-stuttgart.de/~sgehrig/lostAndFoundDataset/gtCoarse.zip). 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
```

## Testing
For testing the algorithm on lost and found dataset, you can run the script in MATLAB:
```
./test.m
```

## Training
For Training the algorithm on lost and found dataset, you can run the script in MATLAB:
```
./train.m
```

## Evaluation

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
```

#### Option
For visualization, to run the script:
```
./result/figuredraw.m
```
The performances are presented as follows:
![performance](./performance.png)

## Reference
```
[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.
```


# News

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.