YOLOv9-FishEye

This repository is the official implementation of paper YOLOv9-FishEye: Improving method for fisheye camera object detection

Performance

Results on FishEye8K dataset.

Model	Test Size	AP50test	mAPtest	F1-score	Param.	FLOPs
Our-E	640	67.2%	46.4%	59.8%	57.0M	186.3G
Our-C	640	63.2%	42.9%	55.1%	24.7M	101.4G
YOLOv9-E	640	64.3%	44.1%	56.7%	57.3M	189.0G
YOLOv9-C	640	60.8%	41.2%	53.7%	25.3M	102.1G
YOLOv8x	640	61.4%	40.29%	51.0%	68.2M	257.8G
YOLOv7-X	640	46.74%	29.19%	57.9%	71.3M	189.9G
YOLOR-W6	1280	64.6%	44.2%	58.9%	79.8M	454.0G
YOLOR-P6	1280	66.32%	44.0%	61.1%	37.2M	326.2G
YOLOv7-E6E	1280	50.8%	32.6%	62.9%	151.7M	843.2G

Our proposed model architecture

Our work is a modified version most inspired from YOLOv9. The target of this modify is tailed for fisheye camera object detection task and also object detection for image from 360 degree camera.

• Our modify model architecture

We replace RepNBottleNeck network in ELAN byRepNLSKBottleNeck by RepNDCNv2BottleNeck and RepNLSKBottleNeck network to get ELAN-DCNv2, ELAN-LSK respectively.

• Our RepNDCNv2BottleNeck network architecture

• Our RepNLSKBottleNeck network architecture

Our proposed loss function

Public after our paper be realeased.

Installation

# Download this repository to local
git clone https://github.com/tuansunday05/fe8kv9

# Install required packages
pip install -r requirements.txt

# Go to code folder
cd /fe8kf9

Evaluation

Extra large version: yolov9-e-modify-converted.pt yolov9-e-modify-trained.pt

Compact version: yolov9-c-modify-converted.pt yolov9-c-modify-trained.pt

# evaluate our yolov9 modify converted models
python val.py --data data/fe8kyolo/data.yaml --img 640 --batch 8 --conf 0.5 --iou 0.5 --device 0 --weights './yolov9-e-modify-converted.pt' --task 'test' --save-json --name yolov9_e_our_640_val

# evaluate our yolov9 modify models
python val_dual.py --data data/fe8kyolo/data.yaml --img 640 --batch 8 --conf 0.5 --iou 0.5 --device 0 --weights './yolov9-e-modify-trained.pt' --task 'test' --save-json --name yolov9_e_our_640_val

# evaluate converted yolov9 models
python val.py --data data/fe8kyolo/data.yaml --img 640 --batch 8 --conf 0.5 --iou 0.5 --device 0 --weights './yolov9-e-converted.pt' --task 'test' --save-json --name yolov9_e_640_val

# evaluate yolov9 models
python val_dual.py --data data/fe8kyolo/data.yaml --img 640 --batch 8 --conf 0.5 --iou 0.5 --device 0 --weights './yolov9-e-trained.pt' --task 'test' --save-json --name yolov9_e_640_val

Training

Data & model preparation

• Download FishEye8K dataset images include train & test set.
• Split train and validation set by using split_data.py
• Create data.yaml in folder like this

names:
  - Bus
  - Bike
  - Car
  - Pedestrian
  - Truck
nc: 5
test: /FE8K/test/images
train: /FE8K/train/images
val: /FE8K/val/images

• Download pre-trained model yolov9-e.pt, yolov9-c.pt.

GPU Training

# train our yolov9-e modify models
python train_dual_custom.py --workers 8 --device 0 --batch 16 --data data/fe8kyolo/data.yaml --img 640 --cfg models/detect/accumulate/yolov9-e-dcn9-lsk-elan4.yaml --weights './yolov9-e.pt' --name yolov9-e-dcn9-lsk-elan4 --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 0

# train our yolov9-c modify models (still in development)
python train_dual_custom.py --workers 8 --device 0 --batch 16 --data data/fe8kyolo/data.yaml --img 640 --cfg models/detect/accumulate/yolov9-c-dcn-lsk-elan4.yaml --weights './yolov9-e.pt' --name yolov9-c-dcn-lsk-elan4 --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 0

Model converting

Because of using auxiliary for more gradient information in early stage, trained YOLOv9 model need to be converted after training to remove auxiliary branch for simple inference and light-weights. After converting the model, the model architecture of YOLOv9 is actually Gelan with respective version.

# convert our yolov9-e modify models
python reparameterization.py --cfg './models/detect/gelan-e-our.yaml' --model 'e' --weights './yolov9-e-modify-trained.pt' --classes_num 5 --save './yolov9-e-modify-converted.pt'

# convert our yolov9-c modify models
python reparameterization.py --cfg './models/detect/gelan-c-our.yaml' --model 'e' --weights './yolov9-c-modify-trained.pt' --classes_num 5 --save './yolov9-c-modify-converted.pt'

Inference

# inference our yolov9 modify converted models
python detect.py --source './figure/example.jpg' --img 640 --device 0 --weights './yolov9-e-modify-converted.pt' --name yolov9_e_modify_640_detect
# inference our yolov9 modify trained models
python detect_dual.py --source './figure/example.jpg' --img 640 --device 0 --weights './yolov9-e-modify-trained.pt' --name yolov9_e_modify_640_detect

Demo with StrongSORT

We also integrated tracking algorithm (StrongSORT) and made a comparison with Yolov9-e model for more intuitive visuallization. Original video demo taking from R0 Fish Len Dataset Center Point.

example_30fps.mp4

Referencess

Expand

• https://github.com/MoyoG/FishEye8K
• https://github.com/WongKinYiu/yolov9
• https://github.com/open-mmlab/mmdetection
• https://github.com/zcablii/LSKNet
• https://github.com/AlexeyAB/darknet
• https://github.com/WongKinYiu/yolor
• https://github.com/WongKinYiu/yolov7
• https://github.com/VDIGPKU/DynamicDet
• https://github.com/DingXiaoH/RepVGG
• https://github.com/ultralytics/yolov5
• https://github.com/meituan/YOLOv6