Attentional Local Contrast Networks for Infrared Small Target Detection

By Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard. IEEE Transactions on Geoscience and Remote Sensing, 2020

744 citations recorded by the source corpus.

To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this article, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the domain knowledge. By designing a feature map cyclic shift scheme, we modularize a conventional local contrast measure method as a depthwise parameterless nonlinear feature refinement layer in an end-to-end network, which encodes relatively long-range contextual interactions with clear physical interpretability. To highlight and preserve the small target features, we also exploit a bottom-up attentional modulation integrating the smaller scale subtle details of low-level features into high-level features of deeper layers. We conduct detailed ablation studies with varying network depths to empirically verify the effectiveness and efficiency of the design of each component in our network architecture. We also compare the performance of our network against other model-driven methods and deep networks on the open SIRST data set as well. The results suggest that our network yields a performance boost over its competitors.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/df0b76bd89e78715b098ee19a24ed36f5fc70f99

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Zhenming Peng
2. Siying Cao
3. Tianfang Zhang
4. Lei Li
5. Tian Pu
6. Jie-Ru Guo
7. Rui Zhang
8. Jing Zhang

In Computer Vision and Pattern Recognition

1. Jocelyn Chanussot
2. Danfeng Hong
3. Xin Wu
4. Lei Li
5. Jie-Ru Guo
6. Yulan Guo
7. Longguang Wang
8. Rui Zhang

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Xin Wu
4. Yulan Guo
5. Longguang Wang
6. Zhenming Peng
7. Wei An
8. Yingqian Wang

Citations in the corpus

• UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, background): “[30], [31] built a bottom-up attention mechanism module and embedded it into U-Net or FPN network structure.”
• ISNet: Shape Matters for Infrared Small Target Detection (2022, methodology): “designed an ACMNet [6] and ALCNet [7] to extract contextual features from different layers.”
• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “[31] further improved their ACM by introducing a dilated local contrast measure.”
• Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background (2023, methodology): “Scholars have also proposed solutions from a variety of perspectives such as generative adversarial networks [20], cross-layer feature fusion, and feature tensor local contrast [21].”