Maritime Object Detection in Aerial Image

Performance

Model	Precision	Recall	mAP(iou=0.5)
YOLOv4	78.3	65.6	64.1
YOLOv5	84.7	76.1	79.0
YOLOv6	86.5	75.8	80.1
YOLOx	86.6	68.2	79.4
YOLOv7	85.3	77.3	80.9
ECA-YOLOv5-EIOU	85.9	77.4	81.1

How to use

Install

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/wyy19990/Maritime-Object-Detection-in-Aerial-Image
$ cd Maritime-Object-Detection-in-Aerial-Image
$ pip install -r requirements.txt

Inference with detect.py

`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest ECA-YOLOv5 release](https://github.com/wyy19990/Maritime-Object-Detection-in-Aerial-Image/releases) and saving results to `runs/detect`.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Training

$ python train.py --data boat.yaml --cfg ECA-yolov5.yaml --weights best.pt --batch-size 64

DataSet

Training set and test set distribution （the path with xx.jpg）

train: ../coco/images/train2017/
val: ../coco/images/val2017/

├── images            # xx.jpg example
│   ├── train2017        
│   │   ├── 000001.jpg
│   │   ├── 000002.jpg
│   │   └── 000003.jpg
│   └── val2017         
│       ├── 100001.jpg
│       ├── 100002.jpg
│       └── 100003.jpg
└── labels             # xx.txt example      
    ├── train2017       
    │   ├── 000001.txt
    │   ├── 000002.txt
    │   └── 000003.txt
    └── val2017         
        ├── 100001.txt
        ├── 100002.txt
        └── 100003.txt

Acknowledgements

https://github.com/ultralytics/yolov5