TY - JOUR
T1 - Lightweight camouflaged object detection model based on multilevel feature fusion
AU - Li, Qiaoyi
AU - Wang, Zhengjie
AU - Zhang, Xiaoning
AU - Du, Hongbao
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/6
Y1 - 2024/6
N2 - 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.
AB - 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.
KW - Camouflaged object detection
KW - Detection speed
KW - Feature extraction network
KW - Lightweight
KW - Multilevel feature fusion
UR - http://www.scopus.com/inward/record.url?scp=85187152701&partnerID=8YFLogxK
U2 - 10.1007/s40747-024-01386-3
DO - 10.1007/s40747-024-01386-3
M3 - Article
AN - SCOPUS:85187152701
SN - 2199-4536
VL - 10
SP - 4409
EP - 4419
JO - Complex and Intelligent Systems
JF - Complex and Intelligent Systems
IS - 3
ER -