MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection

09/28/2022
by   Tianhao Wu, et al.
15

Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and 17598 pixel-level tiny ship annotations. Each image covers about 10000 square kilometers of area with 10000X10000 pixels. Considering the extreme characteristics (e.g., small, dim, changeable) of those tiny ships in such challenging scenes, we propose a multi-level TransUNet (MTU-Net) in this paper. Specifically, we design a Vision Transformer (ViT) Convolutional Neural Network (CNN) hybrid encoder to extract multi-level features. Local feature maps are first extracted by several convolution layers and then fed into the multi-level feature extraction module (MVTM) to capture long-distance dependency. We further propose a copy-rotate-resize-paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning based SIRST methods in terms of probability of detection, false alarm rate and intersection over union.

READ FULL TEXT

page 1

page 3

page 6

page 7

page 10

page 12

page 14

research
06/01/2021

Dense Nested Attention Network for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection aims at separating ...
research
03/21/2020

Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Conducting surveillance of the Earth's orbit is a key task towards achie...
research
03/21/2021

Multi-level Metric Learning for Few-shot Image Recognition

Few-shot learning is devoted to training a model on few samples. Recentl...
research
12/03/2020

D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization

Recently, many detection methods based on convolutional neural networks ...
research
09/29/2020

MARA-Net: Single Image Deraining Network with Multi-level connections and Adaptive Regional Attentions

Removing rain streaks from single images is an important problem in vari...
research
05/18/2023

Multi-spectral Class Center Network for Face Manipulation Detection and Localization

As Deepfake contents continue to proliferate on the internet, advancing ...
research
03/24/2021

Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images

Graphene serves critical application and research purposes in various fi...

Please sign up or login with your details

Forgot password? Click here to reset