A FAST PULSE SIGNAL DETECTION METHOD BASED ON THE MG-YOLOv11n NETWORK
Year: 2026
Type: article
Abstract: This study presents MG-YOLOv11n, a lightweight adaptation of YOLOv11n for rapid detection of pulse signals in time–frequency representations. To improve discrimination between signals with closely related spectral patterns, the network incorporates a multiscale attention module that emphasizes informative regions and feature channels. Computational demands are reduced by substituting selected conventional convolutions with ghost convolutions and removing low-contribution channels through L1-regularization-guided structured pruning. The resulting network is fine-tuned to recover detection performance after compression. Experimental evaluation indicates that MG-YOLOv11n achieves high detection accuracy while requiring fewer model parameters and less computation, supporting efficient analysis of pulse signals.
DOI: 10.61784/jcsee3166
Authors: Peng Zhang, LiangHai Sun
Institution: China University of Petroleum, East China
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7225782552
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