Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 299

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model

Yang, Zhen, Ma, Tianlei, Ku, Yanan, Ma, Qi, Fu, Jun·2022·IEEE Transactions on Instrumentation and Measurement

Abstract

A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tim.2022.3220285

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 27 references

Research works printed but not linked: 9

  1. hui, 2020, A dataset for infrared detection and tracking of dim-small aircraft targets underground/air background, Sci China D, 5, 291
  2. 10.1016/j.infrared.2017.01.009 · doi:10.1016/j.infrared.2017.01.009
  3. 10.1109/LGRS.2019.2954578 · doi:10.1109/lgrs.2019.2954578
  4. 10.1016/j.infrared.2017.12.018 · doi:10.1016/j.infrared.2017.12.018
  5. 10.1109/LGRS.2018.2790909 · doi:10.1109/lgrs.2018.2790909
  6. 10.1016/j.infrared.2014.03.006 · doi:10.1016/j.infrared.2014.03.006
  7. 10.1016/j.patcog.2016.07.036 · doi:10.1016/j.patcog.2016.07.036
  8. 10.1109/TCYB.2015.2501848 · doi:10.1109/tcyb.2015.2501848
  9. 10.1109/TPAMI.2018.2858826 · DOI · Paper page

Cited by

See all 299

Metadata

Authors
Yang, Z., Ma, T., Ku, Y., Ma, Q., & Fu, J.
Venue
IEEE Transactions on Instrumentation and Measurement
Published
2022
Volume
71
Pages
1-15
ISSN
  • 0018-9456 (ISSN-L)
  • 1557-9662
OpenAlex
W4312805004
Cited by
299
References
27
FWCI
32.05
Type
journal article
Language
en
Open access
closed
Funders
  • National Natural Science Foundation of China (61903340)
  • Key Specialized Research and Development Breakthrough in Henan Province (222102210158)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model. https://app.cochat.ai/papers/10.1109/tim.2022.3220285
The paper
Yang, Z., Ma, T., Ku, Y., Ma, Q., & Fu, J. (2022). DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2022.3220285

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.