TY - JOUR
T1 - YOLO Network-Based Extraction of Target Geometry from Time-Frequency Representation
AU - Han, Jingyuan
AU - Guo, Kunyi
AU - Sheng, Xinqing
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - In response to challenges in target geometric parameter extraction from radar echoes, an improved YOLOv8 neural network model is proposed in this paper to recognize target types and extract geometric parameters of targets from time-frequency representation (TFR). Compared with the existing network model, the ability to extract geometric parameters is enhanced by improving the network structure, integrating the attention mechanism, and introducing deformable convolution operators. The attributed scattering center (ASC) models for targets are established to generate the TFRs. This effectively addresses the challenge of generating target datasets with varying geometric parameters for the neural network. The robustness of the network is verified through comparison experiments and ablation experiments for different types of targets. Experimental results, including metric functions and data on reconstructed geometric structures, demonstrate good consistency between the extracted and actual geometric parameters, and the trained network model has a good generalization ability for more complicated structural targets. This study explores the possibility of applying neural networks with TFR to extract target parameters.
AB - In response to challenges in target geometric parameter extraction from radar echoes, an improved YOLOv8 neural network model is proposed in this paper to recognize target types and extract geometric parameters of targets from time-frequency representation (TFR). Compared with the existing network model, the ability to extract geometric parameters is enhanced by improving the network structure, integrating the attention mechanism, and introducing deformable convolution operators. The attributed scattering center (ASC) models for targets are established to generate the TFRs. This effectively addresses the challenge of generating target datasets with varying geometric parameters for the neural network. The robustness of the network is verified through comparison experiments and ablation experiments for different types of targets. Experimental results, including metric functions and data on reconstructed geometric structures, demonstrate good consistency between the extracted and actual geometric parameters, and the trained network model has a good generalization ability for more complicated structural targets. This study explores the possibility of applying neural networks with TFR to extract target parameters.
KW - Neural network
KW - parameter extraction
KW - scattering center (SC)
KW - time-frequency representation (TFR)
UR - http://www.scopus.com/inward/record.url?scp=105002996837&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2025.3550888
DO - 10.1109/ACCESS.2025.3550888
M3 - Article
AN - SCOPUS:105002996837
SN - 2169-3536
VL - 13
SP - 53980
EP - 53995
JO - IEEE Access
JF - IEEE Access
ER -