CoChat
Sign Up
Cited by 29Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesAutomated Road and Building Extraction

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Open access
Li, Xianghui, Li, Xinde, Pan, Hong·2020·IEEE Access
DOI 10.1109/access.2020.3036075
Open this paper in CoChat — free accountOpen PDF

Abstract

Recently, unmanned aerial vehicles (UAV) are widely used in many fields due to the low cost and high flexibility. One of the most popular applications of UAV is vehicle detection in aerial images which plays an important role in traffic surveillance and urban planning. Although, many deep learning based detectors have achieved state-of-the-art (SOTA) performance in natural images, the significant variation in object scales caused by the altitude change of the UAV platform brings great challenges to these detectors for precise localization of vehicles in aerial images. To improve the detection performance for vehicles with different scales, we propose a novel detection algorithm which consists of three stages. In the first stage, to reduce the distortion of vehicles during image resizing and keep more information of aerial images, we utilize an image cropping strategy to divide the image into two patches. In the second stage, we combine the original image and two patches into a batch and detect vehicles with a Convolutional Neural Network (CNN). For feature representation in our detector, we propose Scale-specific Prediction to strengthen the multi-scale features of vehicles with context information. In the final stage, to fuse detections and suppress false alarms, we propose an Outlier-Aware Non-Maximum Suppression. Extensive experiments are conducted to demonstrate the superiority of the proposed algorithm by comparison with other SOTA solutions.

Overview

Integrity signals

Registry

Crossref · 10.1109/access.2020.3036075

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 8 references

Research works printed but not linked: 47

  1. 10.1109/ICPR.2006.479 · DOI · Paper page
  2. 10.1109/CVPRW50498.2020.00103 · DOI
  3. 10.1109/TGRS.2019.2912985 · DOI
  4. 10.1109/TPAMI.2016.2644615 · DOI · Paper page
  5. 10.3390/rs9040368 · DOI
  6. 10.1109/IGARSS.2012.6350403 · DOI
  7. 10.1145/3243394.3243692 · DOI
  8. 10.1109/ICCV.2019.00615 · DOI
  9. 10.3390/rs10010124 · DOI
  10. 10.1109/TCSVT.2018.2874396 · DOI
  11. 10.1109/TGRS.2018.2848243 · DOI
  12. 10.1109/IGARSS.2012.6352585 · DOI
  13. 10.1109/IGARSS.2013.6721127 · DOI
  14. 10.3390/rs11030330 · DOI
  15. 10.1109/JSTARS.2013.2250921 · DOI
  16. redmon, 2018, Yolov3: An incremental improvement, CoRR, abs 1804, 1
  17. 10.1109/CVPR.2017.690 · DOI · Paper page
  18. 10.1109/TPAMI.2020.2981890 · DOI
  19. liu, 2015, SSD: Single shot multibox detector, CoRR, abs 1512 2325, 21
  20. 10.1109/ICIP.2011.6116132 · DOI · Paper page
  21. 10.1109/CVPRW.2019.00084 · DOI
  22. 10.1109/TPAMI.2018.2858826 · DOI · Paper page
  23. bochkovskiy, 2020, YOLOv4: Optimal speed and accuracy of object detection, arXiv 2004 10934
  24. 10.1109/CVPR.2017.557 · DOI
  25. jocher, 2020, Ultralytics/YOLOV5 V3 0
  26. 10.1109/TAES.2017.2732832 · DOI
  27. 10.1109/IROS40897.2019.8967722 · DOI
  28. 10.1109/TGRS.2017.2716984 · DOI
  29. 10.1109/TGRS.2013.2296351 · DOI · Paper page
  30. lin, 2014, Microsoft COCO: Common objects in context, CoRR, abs 1405 312, 740
  31. 10.1109/CVPR.2014.81 · DOI · Paper page
  32. 10.1109/ICCV.2015.169 · DOI · Paper page
  33. dai, 2016, R-FCN: Object detection via region-based fully convolutional networks, Proc 30th Conf Adv Neural Inf Process Syst (NIPS), 29
  34. 10.1109/CVPR.2018.00644 · DOI · Paper page
  35. 10.1109/ACCESS.2019.2915368 · DOI
  36. 10.1109/ACCESS.2019.2903422 · DOI
  37. 10.3390/rs9020100 · DOI
  38. 10.1109/TITS.2018.2797697 · DOI · Paper page
  39. 10.1109/TITS.2016.2517826 · DOI
  40. 10.1109/TPAMI.2010.182 · DOI
  41. 10.1109/ICCV.2019.00972 · DOI · Paper page
  42. 10.3390/rs9040312 · DOI
  43. 10.1007/978-3-030-01249-6_23 · DOI · Paper page
  44. zhou, 2019, Objects as points, CoRR, abs 1904 7850, 1
  45. 10.1007/978-3-662-44851-9_15 · DOI
  46. 10.1007/s11263-014-0733-5 · DOI · Paper page
  47. 10.1109/CVPR.2016.308 · DOI · Paper page

Cited by

See all 29

Metadata

Authors
Li, X., Li, X., & Pan, H.
Affiliations
Southeast University (CN)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
208643-208657
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2020.3036075
OpenAlex
W3095320185
Cited by
29
References
8
Citations by year
2026: 3 · 2025: 3 · 2024: 6 · 2023: 8 · 2022: 6 · 2021: 3
FWCI
1.65
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (62073072, 91748106, 61671151)
  • Advanced Research Project of the 13th Five-Year Plan (31511040301)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
Keywords
vehicle detection, aerial images, unmanned aerial vehicles, multi-scale detection, high-resolution imagery, image cropping, contextual information, traffic monitoring, urban planning

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: Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information. https://app.cochat.ai/papers/10.1109/access.2020.3036075
The paper
Li, X., Li, X., & Pan, H. (2020). Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information. IEEE Access. https://doi.org/10.1109/access.2020.3036075