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LFG-Net for Precise Ship Instance Segmentation in SAR Images

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LFG-Net: Low-level Feature Guided Network for Precise Ship Instance Segmentation in SAR Images

This project hosts the code for reproducing experiment results of LFG-Net

LFG-Net is based on mmdetection framework. Please follow the official guideline of installing the prerequisites

Highlights

  • Small object instance segmentation framework for SAR images.
  • Enhancing the low-level features from image level to instance level.
  • LFG-Net achieves state-of-the-art instance segmentation performance on HRSID, SSDD, and AirSARShip dataset.

Requirements

  • ubuntu == 18.04
  • mmdetection == 2.20
  • mmcv == 1.4.2
  • torch-dct
  • pytorch == 1.7.0, torchvision == 0.8.1

Usage

Training

To train LFG-Net model with original settings of our paper, run:

python train.py

Inference

To inference the trained model with a single gpu, run:

python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox segm

Performance on HRSID

Ship instance segmentation results on HRSID. The models are trained for 12 epochs with the initial learning rate at 0.0025. Results are evaluated with MS COCO evaluation metrics, Parameters, and FPS on Quadaro RTX 6000.

Model AP AP50 AP75 APS APM APL Params. FPS
SOLO 13.8 27.8 13.8 14.2 13.6 3.8 54.92M 14.2
Yolact 35.3 67.2 35.2 34.1 50.0 6.2 34.73M 17.3
Mask R-CNN 52.2 80.6 63.7 51.8 61.3 9.9 43.75M 14.9
Point Rend 53.8 81.8 65.3 53.1 63.3 15.5 55.53M 12.3
GRoIE 52.0 79.7 62.8 51.4 61.1 17.5 47.54M 8.3
Mask Scoring R-CNN 53.0 80.8 63.8 52.6 61.0 11.8 60.01M 14.7
R-ARE-Net 53.6 80.4 65.9 55.3 55.2 13.5 46.58M 10.4
QueryInst 44.2 69.5 53.2 43.4 54.6 12.2 172.22M 4.5
Cascade Mask R-CNN 53.3 82.0 64.0 52.7 61.9 18.3 76.08M 13.5
Hybrid Task Cascade 53.6 82.3 64.7 52.8 63.2 18.6 79.73M 9.7
Detectors 54.1 82.4 65.5 53.3 64.2 20.7 134.00M 6.3
SCNet 54.4 82.4 65.9 54.1 62.1 13.2 94.29M 8.6
LFG-Net 59.7 88.5 72.3 59.7 64.2 11.8 116.78M 6.6
LFG-Net* 63.9 90.1 76.8 63.6 69.5 42.5 174.28M 5.0

Performance on AirSARShip

Ship detection and ship instance segmentation results on AirSARShip dataset. The models are trained for 36 epochs with the initial learning rate at 0.0025. In addition to the MS COCO evaluation metrics, Parameters, and FPS, we also provide the gap between APBbox and APMask.

Model APBbox AP50 AP75 APS APM APL APMask AP50 AP75 APS APM APL Gap Params. FPS
Mask R-CNN 56.8 82.2 64.0 49.4 61.9 25.7 49.1 77.1 56.9 40.1 53.3 30.8 7.7 43.75M 21.8
Point Rend 58.3 83.4 67.1 50.0 63.7 29.7 54.1 80.5 64.0 41.6 59.0 40.9 4.2 55.53M 20.1
GRoIE 57.5 82.0 66.3 49.2 62.9 28.2 51.4 78.7 59.9 40.7 55.8 37.2 6.1 47.54M 10.3
Mask Scoring R-CNN 58.0 83.1 66.1 55.0 63.0 32.2 49.4 77.6 56.5 39.3 53.6 34.0 8.6 60.01M 20.8
R-ARE-Net 56.6 83.3 64.8 49.0 61.9 31.5 53.8 80.4 63.8 46.4 58.5 32.2 2.8 46.58M 12.1
QueryInst 40.1 64.5 42.8 37.3 43.1 25.4 35.4 60.3 38.3 29.1 38.4 31.7 4.7 172.22M 6.4
Cascade Mask R-CNN 60.6 83.3 69.2 50.8 66.0 34.1 50.9 78.4 58.5 40.0 55.4 34.3 9.7 76.80M 18.0
Hybrid Task Cascade 60.7 84.1 69.0 50.9 66.1 36.3 52.7 80.2 61.3 41.5 57.0 39.8 8.0 79.73M 13.6
Detectors 61.7 85.0 69.2 51.5 67.1 37.5 54.5 81.5 63.6 42.6 58.9 42.6 7.2 134.00M 7.7
SCNet 60.1 83.2 67.7 50.8 65.6 32.9 54.3 80.6 63.5 42.8 58.7 42.5 5.8 94.29M 10.5
LFG-Net* 64.8 84.1 73.6 58.8 69.3 39.2 61.8 82.1 70.8 53.8 65.3 53.4 3.0 174.28M 9.0

Citation

If the project helps your research, please cite our paper:

@article{wei2022lfgnet,
title={LFG-Net: Low-level Feature Guided Network for Precise Ship Instance Segmentation in SAR Images},
author={Wei Shunjun, Zeng Xiangfeng, Zhang Hao, Zhou Zichen, Shi Jun, Zhang Xiaoling},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={60},
year={2022},
publisher={IEEE}
}

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