SwinT-YOLOv5s: Improved YOLOv5s for Vehicle-mounted Infrared Target Detection

Xiuli Xin, Feng Pan, Jiacheng Wang, Xiaoxue Feng, Liwei Shao

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

5 Citations (Scopus)

Abstract

Infrared vehicle-mounted target detection is an important research direction in assisted driving, but also a very challenging topic. Existing infrared target detection methods often have problems such as high missed detection rate and false alarm in complex background, small target size and occlusion scene. A SwinT-YOLOv5s algorithm is proposed by the fusion of attention mechanism and convolutional network. Based on YOLOv5s algorithm, a detection layer is added to enhance the detection ability of small target objects. The CBAM modules are inserted into the backbone network to make the model pay more attention to the useful information and resist the interference of redundant information, so as to improve the detection ability in dense scenes. In addition, the Swin Transfomer encoders are used to replace some part of C3 modules to improve the model's ability of mining potential feature details and further improve the detection accuracy of the model. Experimental results show that the improved algorithm increases the average precision (IOU=0.5) and precision rate by 5.60% and 4.20% compared with the original YOLOv5s model, and has good generalization ability in remote small target and overlapping target scenarios.

Original languageEnglish
Title of host publicationProceedings of the 41st Chinese Control Conference, CCC 2022
EditorsZhijun Li, Jian Sun
PublisherIEEE Computer Society
Pages7326-7331
Number of pages6
ISBN (Electronic)9789887581536
DOIs
Publication statusPublished - 2022
Event41st Chinese Control Conference, CCC 2022 - Hefei, China
Duration: 25 Jul 202227 Jul 2022

Publication series

NameChinese Control Conference, CCC
Volume2022-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference41st Chinese Control Conference, CCC 2022
Country/TerritoryChina
CityHefei
Period25/07/2227/07/22

Keywords

  • CBAM
  • Object Detection
  • Swin Transformer Encoder
  • YOLOv5s algorithm
  • infrared image

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