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ACM multimedia asia 2023 - Grand Challenge: Embedded AI Object Detection Model Design Contest on Fish-eye Around-view Cameras

PAIR-LITEON Competition: Embedded AI Object Detection Model Design Contest on Fish-eye Around-view Cameras (Team name: yuhsi44165)

PAIR-LITEON Competition: Embedded AI Object Detection Model Design Contest on Fish-eye Around-view Cameras(Final Competition) (Team name: yuhsi44165)

$\large{\textbf{Abstract}}$

This challenge is divided into two stages: qualification and final competition. We will acquire regular image data and need to perform detection on images with a fisheye effect. The approach described in this context begins by taking the original images and transforming them to mimic fisheye effect images for training. Furthermore, this challenge imposes limitations on computational resources, so striking a balance between accuracy and speed is a crucial aspect. In this paper, we asserted that our approach for this competition can achieve high performance with just one epoch of training. In summary, we achieved the top position among 24 participating teams in the qualification competition and secured the fourth position among the 11 successful submitted teams in the final competition.

1. Environmental Setup

Hardware Information
  • CPU: Intel® Core™ i7-11700F
  • GPU: GeForce GTX 1660 SUPER™ VENTUS XS OC (6G)
Create Conda Environments 
$ conda create -n yolov7 python=3.9 -y
$ conda activate yolov7
$ git clone https://github.com/WongKinYiu/yolov7.git
$ cd yolov7/
$ pip install -r requirements.txt
$ pip install scikit-learn
Create Virtual Environments 
$ sudo apt-get install python3-virtualenv
$ virtualenv --version    # check version
$ python3.8 -m venv ~/mx
$ . ~/mx/bin/activate
$ pip3 install --upgrade pip wheel
$ pip3 install --extra-index-url https://developer.memryx.com/pip memryx
>> eneural_event
>> memryx23
$ cd ~/mx/
$ pip install -r requirements.txt

2. Reproducing Details

Dataset Description

Restricted classes (a): vehicle→1, pedestrian→2, scooter→3, bicycle→4

Datasets iVS-Dataset FishEye8K Valeo Datasets-L (F+V)
Classes (a) Bus:1→1, Bike:2→3, Car:3→1, Pedestrian:4→2, Truck:5→1 vehicles:1→1, person:2→2, bicycle:3→4 (a)
# Train img 89002 5288 6587 11875
# Val img 2700 2712 1647 4359
# Test img 2700 -- -- --
# Train labels [153928, 497843, 74806, 9690] [153928, 497843, 74806, 9690, 0] [35464, 12936, 5593] [81285, 22440, 70751, 5652]
# Val labels [3522, 12856, 994, 25] [3522, 12856, 994, 25, 0] [8940, 3313, 1460] [20187, 5568, 17780, 1401]
# Test labels [3532, 12638, 1010, 21] -- -- --
# Total labels 770865 157358 67706 225064

Data Augmentation

Augmented Datasets Datasets-fp Datasets-fp-f Datasets-fp-r Datasets_fp-r-f
Classes (a) (a) (a) (a)
# Train img 151042 151042 453126 453126
# Val img 37762 37762 113286 113286
# Test img -- -- -- --
# Train labels [257388, 836476, 122394, 15678] [257388, 836476, 122394, 15678] [1029552, 3345904, 489576, 62712] [1029552, 3345904, 489576, 62712]
# Val labels [64576, 210198, 31226, 3794] [64576, 210198, 31226, 3794] [258304, 840792, 124904, 15176] [258304, 840792, 124904, 15176]
# Test labels -- -- -- --
# Total labels 1541730 1541730 6166920 6166920
Folder Structure on Local Machine

  • Create the following folder structure on the local machine

    # Qualification Competition
qualification/
├── yolov7/
    ├── requirements.txt
    ├── submit.py
    └── test.py
└── preprocess/
    ├── xml2txt.py
    ├── folderStructure.py
    ├── resplit.py
    ├── fisheye
    ├── data_aug.py
    ├── data_aug_2.py
    └── statistics.py

# Final Competition
mx/
├── requirements.txt
├── calculate.py
├── cal_model_size.py
├── cal_model_complexity.py
├── run_detection_pt.py
├── run_detection_onnx.py
├── best.csv
└── best.txt
Qualification Competition
Final Competition

3. Demonstration

3.1. Comparison between the unaltered image and the fisheye-distorted image

3.2. Contrast between artificially fisheye-distorted and fisheye camera-captured image

SwingNet

4. Leaderboard Scores

4.1. Qualification Competition

Leaderboards Filename Upload time Evaluation result Ranking
Public & Private fp-1-0.01-0.5-2172.csv 2023-08-04 00:51:42 0.5700583 1/24

4.2. Final Competition

Team Score Accuracy Model Complexity GFLOPs Model size MB Speed ms Ranking
yuhsi44165 26.60 11.23% 195.91 283.34 114.80 4/11

5. GitHub Acknowledgement

6. References

Citation

@inproceedings{chen2023one,
  title={One-Epoch Training for Object Detection in Fisheye Images},
  author={Chen, Yu-Hsi},
  booktitle={Proceedings of the 5th ACM International Conference on Multimedia in Asia},
  pages={1--5},
  year={2023}
}