Lightweight YOLO models for object detection based on low-rank decomposition

As train intelligence continues to advance, numerous studies have emerged to explore lightweight techniques for object detection models on onboard equipment, for the purpose of improving calculation efficiency amidst limited resources. This paper proposed a parameter compression algorithm based on l...

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Κύριοι συγγραφείς:	LIN Delü, LIU Chang, CHEN Qi, ZENG Yang, HE Kun
Μορφή:	Artigo
Γλώσσα:	Chinês
Έκδοση:	Editorial Department of Electric Drive for Locomotives 2024-01-01
Σειρά:	机车电传动
Θέματα:	deep learning low-rank decomposition automatic train operation lightweight treatment of model object detection
Διαθέσιμο Online:	http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.120
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