A Context-Aware Anchor-free Tiny Object Detector for Aerial Images

Li Syuan Chen, Der Lor Way, Zen Chung Shih

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Object detection in aerial images is a task of predicting the target categories while locating the objects. Since the different categories of objects may have similar shapes and textures in aerial images, we propose context-aware layer to provide global and robust features for classification and regression branch. In addition, we propose the CentraBox to reduce unnecessary training samples during the training phase. We also propose the instance-level normalization to balance the contributions among the instances. Finally, we compare our method with other methods in terms of accuracy, speed and parameters usage. Moreover, we also compare our own method with different hyper-parameter settings.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2022
EditorsMasayuki Nakajima, Shogo Muramatsu, Jae-Gon Kim, Jing-Ming Guo, Qian Kemao
PublisherSPIE
ISBN (Electronic)9781510653313
DOIs
StatePublished - 4 Jan 2022
Event2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 - Hong Kong, China
Duration: 4 Jan 20226 Jan 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12177
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2022 International Workshop on Advanced Imaging Technology, IWAIT 2022
Country/TerritoryChina
CityHong Kong
Period4/01/226/01/22

Keywords

  • Aerial image
  • Object detection
  • Self attention

Fingerprint

Dive into the research topics of 'A Context-Aware Anchor-free Tiny Object Detector for Aerial Images'. Together they form a unique fingerprint.

Cite this