VAID(Vehicle Aerial Imaging from Drone) dataset contains 6000 aerial images under different illumination conditions, viewing angles from different places in Taiwan.The images are taken with the resolution of 1137 * 640 pixels in JPG format. Our VAID fataset contains seven classes of Vehicles, namely 'sedan' , 'minibus' , 'truck' , 'pickup truck' , 'bus' , 'cement truck' , ' trailer'.
From left to right in the figure below are labels 'sedan' , 'minibus' , 'truck' , 'pickup truck' , 'bus' , 'cement truck' , ' trailer'
You can download VAID dataset in this website.https://vision.ee.ccu.edu.tw/aerialimage/
We test VAID dataset on 5 common model including Faster R-CNN, Yolov4, MobileNetv3 , RefineDet and U-Net.
environment : tensorflow1.4 cuda9.2
- Download the file https://drive.google.com/drive/folders/1S35gyY4c2B6HGzgB8WRl_scDYWdukhTb?usp=sharing
- Put for_download/faster rcnn/data and for_download/faster rcnn/output into VAID_dataset/tf-faster-rcnn
- Download dataset and put the fold Annotations amd JPEGImages into VAID_dataset/tf-faster-rcnn/data/VOCdevkit2007/VOC2007
- make at VAID_dataset/tf-faster-rcnn/lib and VAID_dataset/tf-faster-rcnn/data/coco/PythonAPI
command :
demo
GPU_ID=0
CUDA_VISIBLE_DEVICES=${GPU_ID} ./tools/demo.py --net res101 --dataset pascal_voc
train
./experiments/scripts/train_faster_rcnn.sh 0 pascal_voc res101
test
./experiments/scripts/test_faster_rcnn.sh 0 pascal_voc res101
environment : cuda10.0 , cudnn>7.0 , opencv>2.4
- Download the file https://drive.google.com/drive/folders/1S35gyY4c2B6HGzgB8WRl_scDYWdukhTb?usp=sharing
- Put the folders for_download/yolov4/3rdparty , for_download/yolov4/backup , for_download/yolov4/cfd , for_download/yolov4/data , for_download/yolov4/include and for_download/yolov4/src into VAID_dataset/Yolov4/darknet
- make in VAID_dataset/Yolov4/darknet
- ./darknet if you succeed to make ,you will see usage: ./darknet
- put for_download/yolov4/yolov4.conv.137 into VAID_dataset/Yolov4/darknet 6.Download dataset and put the fold Annotations amd JPEGImages into VAID_dataset-master/Yolov4/darknet/VOCdevkit VOC2007 and VAID_dataset-master/Yolov4/darknet/VOCdevkit VOC2007_test
command:
train
./darknet detector train cfg/voc.data cfg/yolo-obj.cfg yolov4.conv.137
calculate
map
./darknet detector map cfg/voc.data cfg/yolo-obj.cfg backup/yolo-obj_best.weights
test
./darknet detector test cfg/voc.data cfg/yolo-obj.cfg backup/yolo-obj_best.weights
If you want to know more command , you can see the official yolo github.
environment : pytorch 1.4.0 cuda9.2 Download dataset and put the fold Annotations amd JPEGImages into VAID_dataset-master/MobileNetV3-SSD-Compact-Version/VAID run create_data_lists.py
command
python train.py
python eval.py
environment : cuda9.2 , pytorch 1.4.0
- modify home path in VAID_dataset-master/RefineDet.PyTorch-master/data/config.py
- modify VOC_ROOT path in VAID_dataset-master/RefineDet.PyTorch-master/data/voc0712.py
- Download dataset and put the fold Annotations amd JPEGImages into VAID_dataset-master/RefineDet.PyTorch-master/data/VAID
- Download the file https://drive.google.com/drive/folders/1S35gyY4c2B6HGzgB8WRl_scDYWdukhTb?usp=sharing
- put for_download/Refinedet/weights into VAID_dataset-master/RefineDet.PyTorch-master
command:
train
./train_refinedet320.sh
calculate map
./eval_refinedet.sh
environment : cuda 10.0 , Keras 2.2.4
1.Download the file https://drive.google.com/drive/folders/1S35gyY4c2B6HGzgB8WRl_scDYWdukhTb?usp=sharing 2.Put for_download/U-Net/data and for_download/U-Net/model into VAID_dataset/U-Net/ ; put for_download/U-Net/results into VAID_dataset/U-Net/MAP/ ; put for_download/U-Net/detection-results into VAID_dataset/U-Net/MAP/car/
3.Train(unet+_residual_vgg16.py) (1)modify 'path' to VAID_dataset/U-Net/data/train_val(seperate)/ (2)modify the model name you want to save in 'model_checkpoint'
4.Test(test.py) (1)modify 'test_path' to VAID_dataset/U-Net/data/test/test_jpg/ (2)modify 'label_save_dir' to VAID_dataset/U-Net/data/predict/predict_label/ (3)modify 'visualize_save_dir' to VAID_dataset/U-Net/data/predict/predict_visualize/ (4)modify 'modelFile' to model file(model_name.hdf5) in 'model' folder
5.IOU(evaluate_iou.py) (1)modify 'test_label_path' to VAID_dataset/U-Net/data/test/test_label/ (2)modify 'predict_path' to VAID_dataset/U-Net/data/predict/predict_label/
6.mAP(main_map.py)
The code are from the following website.
Faster R-CNN : https://github.com/endernewton/tf-faster-rcnn
Yolov4 : https://github.com/AlexeyAB/darknet
Mobilenet : https://github.com/shaoshengsong/MobileNetV3-SSD-Compact-Version
RefineDet : https://github.com/luuuyi/RefineDet.PyTorch