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

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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Bibliographic Details
Main Authors: Haohui Lv, Hanbing Yan, Keyang Liu, Zhenwu Zhou, Junjie Jing
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Sensors
Subjects:
pedestrian detection
deep learning
model pruning
context extraction module
attention module
DIoU_NMS
Online Access:https://www.mdpi.com/1424-8220/22/15/5903

Internet

https://www.mdpi.com/1424-8220/22/15/5903

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