Lightweight camouflaged object detection model based on multilevel feature fusion

Qiaoyi Li, Zhengjie Wang*, Xiaoning Zhang, Hongbao Du

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The intrinsic similarity between camouflaged objects and background environment impedes the automatic detection/segmentation of camouflaged objects, and novel network architectures for deep learning are promising to overcome this challenge and improve detection accuracy. However, these existing network architectures for distinguishing between camouflaged objects and their backgrounds do not account for the constraint of detection speed, which results in high computational complexity and the inability to meet the requirements of rapid detection. Therefore, based on the human visual system, this study proposes a single-stage lightweight camouflage object detection network using multilevel feature fusion, integrating features of various feature layers and receptive field sizes. Using three benchmark datasets for normal camouflaged objects, the lightweight network (LINet) model demonstrated an accuracy superior to those of six existing mainstream camouflaged object detection methods. Its detection speed, 126.3 frames per second, is significantly higher than those of the existing mainstream methods, enabling rapid detection with a maximum increase of 187.62%. The accuracy of LINet is the minimum and maximum for Resnet101 and Resnet152, respectively. These findings pave the way for diverse applications of camouflaged target detection algorithms.

Original languageEnglish
Pages (from-to)4409-4419
Number of pages11
JournalComplex and Intelligent Systems
Volume10
Issue number3
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Camouflaged object detection
  • Detection speed
  • Feature extraction network
  • Lightweight
  • Multilevel feature fusion

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Li, Q., Wang, Z., Zhang, X., & Du, H. (2024). Lightweight camouflaged object detection model based on multilevel feature fusion. Complex and Intelligent Systems, 10(3), 4409-4419. https://doi.org/10.1007/s40747-024-01386-3