Object Detection for VisDrone(无人机航拍图像目标检测)
My environment
1、Windows10 (Linux available)
2、tensorflow >= 1.12.0
3、python3.6 (anaconda)
4、cv2
5、ensemble-boxes(pip install ensemble-boxes)
Datasets(XML format for training set)
(1).Datasets is available on https://github.com/VisDrone/VisDrone-Dataset
(2).Please download xml annotations on Baidu Yun (提取码: ia3f), or Google Drive, and configure it in ./core/config/cfgs.py
(3).You can also use ./data/visdrone2xml.py to generate your visdrone xml files, modify the path information.
training-set format:
├── VisDrone2019-DET-train
│ ├── Annotation(xml format)
│ ├── JPEGImages
Pretrained Models(ResNet50vd, 101vd)
Please download pretrained models on Baidu Yun (提取码: krce), or Google Drive, then put it into ./data/pretrained_weights
Train
Modify the parameters in ./core/config/cfgs.py
python train_step.py
Eval
Modify the parameters in ./core/config/cfgs.py
python eval_visdrone.py, it will get txt format file, then use official matlab tools to eval the final results.
python eval_model_ensemble.py. Before the running of this file, you should set NORMALIZED_RESULTS_FOR_MODEL_ENSEMBLE=True in cfgs.py and then run eval_visdrone.py to get normalized txt result.
Visualization
Modify the parameters in ./core/config/cfgs.py
python image_demo.py, it will get visualized results.
Visualized Result (multi-scale training+multi-scale testing) 
Test Result(Validation set)：
1. ResNet50-vd
| Name | maxDets | Result(s/m) |
|---|---|---|
| Average Precision (AP) @( IoU=0.50:0.95) | maxDets=500 | 31.26%/35.1% |
| Average Precision (AP) @( IoU=0.50 ) | maxDets=500 | 56.44%/60.29% |
| Average Precision (AP) @( IoU=0.75 ) | maxDets=500 | 30.13%/35.42% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets= 1 | 0.78%/0.58% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets= 10 | 6.62%/6.05% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets=100 | 38.21%/40.99% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets=500 | 48.41%/53% |
"s" means single-scale training + single-scale testing; "m"means multi-scale training + multi-scale testing
2. ResNet101-vd
| Name | maxDets | Result(s/m) |
|---|---|---|
| Average Precision (AP) @( IoU=0.50:0.95) | maxDets=500 | 31.7%/35.98% |
| Average Precision (AP) @( IoU=0.50 ) | maxDets=500 | 56.94%/61.64% |
| Average Precision (AP) @( IoU=0.75 ) | maxDets=500 | 30.59%/36.13% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets= 1 | 0.67%/0.61% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets= 10 | 6.29%/6.13% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets=100 | 38.66%/42.33% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets=500 | 49.29%/53.68% |
3. Model Ensemble (ResNet101-vd+ResNet50-vd)
| Name | maxDets | Result |
|---|---|---|
| Average Precision (AP) @( IoU=0.50:0.95) | maxDets=500 | 36.76% |
| Average Precision (AP) @( IoU=0.50 ) | maxDets=500 | 62.33% |
| Average Precision (AP) @( IoU=0.75 ) | maxDets=500 | 37.41% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets= 1 | 0.59% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets= 10 | 6.06% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets=100 | 42.57% |
| Average Recall (AR) @( IoU=0.50:0.95) | maxDets=500 | 54.53% |
You can download trained weights(ResNet50vd, 101vd) on Baidu Yun (提取码: 9u9m), or Google Drive, then put it into ./saved_weights
Reference
1、https://github.com/DetectionTeamUCAS/Faster-RCNN_Tensorflow
2、https://github.com/open-mmlab/mmdetection
3、https://github.com/ZFTurbo/Weighted-Boxes-Fusion
4、https://github.com/kobiso/CBAM-tensorflow-slim
5、https://github.com/SJTU-Thinklab-Det/DOTA-DOAI
6、https://github.com/Viredery/tf-eager-fasterrcnn
7、https://github.com/VisDrone/VisDrone2018-DET-toolkit
8、https://github.com/YunYang1994/tensorflow-yolov3
9、https://github.com/zhpmatrix/VisDrone2018
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