Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision

Pytorch implementation of our Label Evolution with Single Point Supervision (LESPS). [Paper]   [Web]

News: We recommend our newly-released repository BasicIRSTD, an open-source and easy-to-use toolbox for infrared small target detction. [link]

Overview

The Mapping Degeneration Phenomenon

Fig. 1. Illustrations of mapping degeneration under point supervision. CNNs always tend to segment a cluster of pixels near the targets with low confidence at the early stage, and then gradually learn to predict groundtruth point labels with high confidence.

Fig. 2. Quantitative and qualitative illustrations of mapping degeneration in CNNs.

The Label Evolution Framework

Fig. 3. Illustrations of Label Evolution with Single Point Supervision (LESPS). During training, intermediate predictions of CNNs are used to progressively expand point labels to mask labels. Black arrows represent each round of label updates.

Requirements

• Python 3
• pytorch (1.2.0), torchvision (0.4.0) or higher
• numpy, PIL
  
  

Datasets

• NUAA-SIRST   [download dir]   [paper]
• NUDT-SIRST   [download dir]   [paper]
• IRSTD-1K   [download dir]   [paper]

SIRST3 is used for training, and is a combination of NUAA-SIRST, NUDT-SIRST, IRSTD-1K datasets. Please first download datasets via Baidu Drive (key:1113), and place the datasets to the folder ./datasets/.

To gnenrate centroid annoation, run matlab cpde centroid_anno.m

To gnenrate coarse annoation, run matlab cpde coarse_anno.m

• Our project has the following structure:

  ├──./datasets/SIRST3/
  │    ├── images
  │    │    ├── XDU0.png
  │    │    ├── Misc_1.png
  │    │    ├── ...
  │    │    ├── 001327.png
  │    ├── masks
  │    │    ├── XDU0.png
  │    │    ├── Misc_1.png
  │    │    ├── ...
  │    │    ├── 001327.png
  │    ├── masks_centroid
  │    │    ├── XDU0.png
  │    │    ├── Misc_1.png
  │    │    ├── ...
  │    │    ├── 001327.png
  │    ├── masks_coarse
  │    │    ├── XDU0.png
  │    │    ├── Misc_1.png
  │    │    ├── ...
  │    │    ├── 001327.png
  │    ├── img_idx
  │    │    ├── train_SIRST3.txt
  │    │    ├── test_SIRST3.txt  
  │    │    ├── test_NUAA-SIRST.txt
  │    │    ├── test_NUDT-SIRST.txt
  │    │    ├── test_IRSTD-1K.txt
  

Train

python train.py --model_names ['DNANet', 'ALCNet', 'ACM'] --dataset_names ['SIRST3'] --label_type 'centroid'

python train.py --model_names ['DNANet', 'ALCNet', 'ACM'] --dataset_names ['SIRST3'] --label_type 'coarse'

Test

python test.py --model_names ['DNANet', 'ALCNet', 'ACM'] --pth_dirs None --dataset_names ['NUAA-SIRST', 'NUDT-SIRST', 'IRSTD-1K']

python test.py --model_names ['DNANet', 'ALCNet', 'ACM'] --pth_dirs ['SIRST3/DNANet_full.pth.tar', 'SIRST3/DNANet_LESPS_centroid.pth.tar', 'SIRST3/DNANet_LESPS_coarse.pth.tar','SIRST3/ALCNet_full.pth.tar', 'SIRST3/ALCNet_LESPS_centroid.pth.tar', 'SIRST3/ALCNet_LESPS_coarse.pth.tar','SIRST3/ACM_full.pth.tar', 'SIRST3/ACM_LESPS_centroid.pth.tar', 'SIRST3/ACM_LESPS_coarse.pth.tar']--dataset_names ['NUAA-SIRST', 'NUDT-SIRST', 'IRSTD-1K']

Model Analyses

Analyses of Mapping Degeneration

Fig. 4. IoU and visualize results of mapping degeneration with respect to different characteristics of targets (i.e.,(a) intensity, (b) size, (c) shape, and (d) local background clutter) and point labels (i.e.,(e) numbers and (f) locations). We visualize the zoom-in target regions of input images with GT point labels (i.e., red dots in images) and corresponding CNN predictions (in the epoch reaching maximum IoU).

Analyses of the Label Evolution Framework

Effectiveness

Table 1. Average results achieved by DNAnet with (w/) and without (w/o) LESPS under centroid, coarse point supervision together with full supervision.

Fig. 5. Quantitative and qualitative results of evolved target masks.

Fig. 6. Visualizations of regressed labels during training and network predictions during inference with centroid and coarse point supervision.

Parameters

Fig. 7. PA (P) and IoU (I) results of LESPS with respect to (a) initial evolution epoch, (b) Tb and (c) k of evolution threshold, and (d) evolution frequency.

Comparison Results

Comparison to SISRT Detection Methods

Table 2. Results of different methods. “CNN Full”, “CNN Centroid”, and “CNN Coarse” represent CNN-based methods under full supervision, centroid and coarse point supervision. “+” represents CNN-based methods equipped with LESPS.

Fig. 8. Visual detection results of different methods. Correctly detected targets and false alarms are highlighted by red and orange circles, respectively.

Comparison to Fixed Pseudo Labels

Table 3. Results of DNA-Net trained with pseudo labels generated by input intensity threshold, LCM-based methods and LESPS under centroid and coarse point supervision. Best results are shown in boldface.

Citiation

@article{LESPS,
  author = {Ying, Xinyi and Liu, Li and Wang, Yingqian and Li, Ruojing and Chen, Nuo and Lin, Zaiping and Sheng, Weidong and Zhou, Shilin},
  title = {Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision},
  journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
}

Contact

Welcome to raise issues or email to yingxinyi18@nudt.edu.cn for any question.