YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection

In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate...

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Principais autores:	Haohui Lv, Hanbing Yan, Keyang Liu, Zhenwu Zhou, Junjie Jing
格式:	Artigo
語言:	Inglês
出版:	MDPI AG 2022-08-01
叢編:	Sensors
主題:	pedestrian detection deep learning model pruning context extraction module attention module DIoU_NMS
在線閱讀:	https://www.mdpi.com/1424-8220/22/15/5903
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