Best Papers
 The Object Detector for Aerial Image Using High Resolution Feature Extractor and Attention Module 


Vol. 48,  No. 1, pp. 1-11, Jan.  2023
10.7840/kics.2023.48.1.1


PDF Full-Text
  Abstract

Object detectors, such as YOLOv5, achieve high performance on datasets that consist of objects in everday scenes, like the COCO dataset. However, it shows poor performance in aerial images because the detectors did not consider the size of the objects. First, the aerial images contain very tiny objects and these objects are densely located in a image. Second, because of wide FOV, most of images has a lot of complex background information. It makes object detector very difficult to recognize object and background. In this paper, we propose an object detector that focuses on tiny objects with high resolution feature maps and attention network. We densely located in a image. Second, because of wide FOV, most of images has a lot of complex background information. It makes object detector very difficult to recognize object and background. In this paper, we propose an object detector that focuses on tiny objects with high resolution feature maps and attention network. We design SB network which is feature extractor through high resolution feature map. Also we adopted Triplet Attention to TA network for distinguish between objects and background. The proposed YOLOv5l-TA network and achieves  11.2% higher than YOLOv5l baseline network and 280%, 55%, 36.1%, 4.8% in  ,   ,  ,  metrics.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

H. Kim, J. Ahn, T. Lee, B. Choi, "The Object Detector for Aerial Image Using High Resolution Feature Extractor and Attention Module," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 1, pp. 1-11, 2023. DOI: 10.7840/kics.2023.48.1.1.

[ACM Style]

HaeMoon Kim, JongSik Ahn, Tae-Young Lee, and Byungin Choi. 2023. The Object Detector for Aerial Image Using High Resolution Feature Extractor and Attention Module. The Journal of Korean Institute of Communications and Information Sciences, 48, 1, (2023), 1-11. DOI: 10.7840/kics.2023.48.1.1.

[KICS Style]

HaeMoon Kim, JongSik Ahn, Tae-Young Lee, Byungin Choi, "The Object Detector for Aerial Image Using High Resolution Feature Extractor and Attention Module," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 1, pp. 1-11, 1. 2023. (https://doi.org/10.7840/kics.2023.48.1.1)
Vol. 48, No. 1 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880