ISTDU-Net
November 25, 2023 · View on GitHub
code of article ISTDU-Net：Infrared Small-Target Detection U-Net
Introduction
This code using Python3.8+Pytorch1.8 is the complete code of article ISTDU-Net：Infrared Small-Target Detection U-Net, suitable for Ubuntu system.
Demo
demo.py：Input picture or video path, frame by frame output algorithm results(the code used to save the results is masked by default, please make your own modifications as required) The only thing that needs to be changed is the path: Line10 : videoPath = './test/images' ##change to the path where you want to test the image
The trained weight path is(already configured in this code)： ./save_pth/ISTDU_Net/best.pth
！！！Note：img, _ = processGray(img, scale=scale, inp_h=inpShape[1], inp_w=inpShape[0]) This function automatically fills images of any size to adapt to the input of the network. Therefore, the larger the input image, the larger the GPU resources will be consumed. The input image should preferably be 512 x 512 pixels, a graphics card of 2080Ti is enough.
Train
The train code is update:train_DS.py
The link to download the paper
https://doi.org/10.1109/LGRS.2022.3141584
Reference
Q. Hou, L. Zhang, F. Tan, Y. Xi, H. Zheng and N. Li, "ISTDU-Net: Infrared Small-Target Detection U-Net," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 7506205, doi: 10.1109/LGRS.2022.3141584.