sar_vessel_detect
Code for the AI2 Skylight team's submission in the xView3 competition (https://iuu.xview.us) for vessel detection in Sentinel-1 SAR images. See whitepaper.pdf for a summary of our approach.
Dependencies
Install dependiences using conda:
cd sar_vessel_detect/
conda env create -f environment.yml
Pre-processing
First, ensure that training and validation scenes are extracted to the same directory, e.g. /xview3/all/images/. The training and validation labels should be concatenated and written to a CSV file like /xview3/all/labels.csv.
Prior to training, the large scenes must be split up into 800x800 windows (chips). Set paths and parameters in data/configs/chipping_config.txt, and then run:
cd sar_vessel_detect/src/
python -m xview3.processing.preprocessing ../data/configs/chipping_config.txt
Initial Training
We first train a model on the 50 xView3-Validation scenes only. We will apply this model in the xView3-Train scenes, and incorporate high-confidence predictions as additional labels. This is because xView3-Train scenes are not comprehensively labeled since most labels are derived automatically from AIS tracks.
To train, set paths and parameters in data/configs/initial.txt, and then run:
python -m xview3.training.train ../data/configs/initial.txt
Apply the trained model in xView3-Train, and incorporate high-confidence predictions as additional labels:
python -m xview3.infer.inference --image_folder /xview3/all/images/ --weights ../data/models/initial/best.pth --output out.csv --config_path ../data/configs/initial.txt --padding 400 --window_size 3072 --overlap 20 --scene_path ../data/splits/xview-train.txt
python -m xview3.eval.prune --in_path out.csv --out_path out-conf80.csv --conf 0.8
python -m xview3.misc.pred2label out-conf80.csv /xview3/all/chips/ out-conf80-tolabel.csv
python -m xview3.misc.pred2label_concat /xview3/all/chips/chip_annotations.csv out-conf80-tolabel.csv out-conf80-tolabel-concat.csv
python -m xview3.eval.prune --in_path out-conf80-tolabel-concat.csv --out_path out-conf80-tolabel-concat-prune.csv --nms 10
python -m xview3.misc.pred2label_fixlow out-conf80-tolabel-concat-prune.csv
python -m xview3.misc.pred2label_drop out-conf80-tolabel-concat-prune.csv out.csv out-conf80-tolabel-concat-prune-drop.csv
mv out-conf80-tolabel-concat-prune-drop.csv ../data/xval1b-conf80-concat-prune-drop.csv
Final Training
Now we can train the final object detection model. Set paths and parameters in data/configs/final.txt, and then run:
python -m xview3.training.train ../data/configs/final.txt
Attribute Prediction
We use a separate model to predict is_vessel, is_fishing, and vessel length.
python -m xview3.postprocess.v2.make_csv /xview3/all/chips/chip_annotations.csv out.csv ../data/splits/our-train.txt /xview3/postprocess/labels.csv
python -m xview3.postprocess.v2.get_boxes /xview3/postprocess/labels.csv /xview3/all/chips/ /xview3/postprocess/boxes/
python -m xview3.postprocess.v2.train /xview3/postprocess/model.pth /xview3/postprocess/labels.csv /xview3/postprocess/boxes/
Inference
Suppose that test images are in a directory like /xview3/test/images/. First, apply the object detector:
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20
python -m xview3.eval.prune --in_path out.csv --out_path out-prune.csv --nms 10
Now apply the attribute prediction model:
python -m xview3.postprocess.v2.infer /xview3/postprocess/model.pth out-prune.csv /xview3/test/chips/ out-prune-attribute.csv attribute
Test-time Augmentation
We employ test-time augmentation in our final submission, which we find provides a small 0.5% performance improvement.
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-1.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-2.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20 --fliplr True
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-3.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20 --flipud True
python -m xview3.infer.inference --image_folder /xview3/test/images/ --weights ../data/models/final/best.pth --output out-4.csv --config_path ../data/configs/final.txt --padding 400 --window_size 3072 --overlap 20 --fliplr True --flipud True
python -m xview3.eval.ensemble out-1.csv out-2.csv out-3.csv out-4.csv out-tta.csv
python -m xview3.eval.prune --in_path out-tta.csv --out_path out-tta-prune.csv --nms 10
python -m xview3.postprocess.v2.infer /xview3/postprocess/model.pth out-tta-prune.csv /xview3/test/chips/ out-tta-prune-attribute.csv attribute
Confidence Threshold
We tune the confidence threshold on the validation set. Repeat the inference steps with test-time augmentation on the our-validation.txt split to get out-validation-tta-prune-attribute.csv. Then:
python -m xview3.eval.metric --label_file /xview3/all/chips/chip_annotations.csv --scene_path ../data/splits/our-validation.txt --costly_dist --drop_low_detect --inference_file out-validation-tta-prune-attribute.csv --threshold -1
python -m xview3.eval.prune --in_path out-tta-prune-attribute.csv --out_path submit.csv --conf 0.3 # Change to the best confidence threshold.
Inquiries
For inquiries, please open a Github issue.
3 Dec 13, 2021
13 Jun 24, 2022
49 Dec 27, 2022
80 Nov 30, 2022
8 Dec 04, 2022
1 Nov 24, 2022
30 Jan 02, 2023
71 Jan 02, 2023
69 Dec 10, 2022
0 Nov 09, 2022
26 Dec 13, 2022
293 Dec 30, 2022
200 Dec 28, 2022
876 Dec 09, 2022
116 Jan 03, 2023
41 Oct 29, 2022
75 Nov 27, 2022
384 Nov 29, 2022
3.4k Dec 29, 2022
49 Nov 23, 2022