Open AccessYolov5-based channel pruning is used for real-time detection of construction workers’ safety helmets and anti-slip shoes in informationalized construction sites
Author(s) -
Xuehui Shen,
Baojiang Li,
Xichao Wang,
Guochu Chen,
Junquan Lu,
Bin Lin,
Jianming Zheng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012027
Subject(s) - channel (broadcasting) , pruning , slip (aerodynamics) , enhanced data rates for gsm evolution , computer science , real time computing , simulation , artificial intelligence , engineering , computer network , aerospace engineering , agronomy , biology
In view of the problems that the application of target detection model in edge equipment occupied too much memory and the operation was slow, a lightweight model based on YoloV5 is proposed to detect the wearing condition of safety helmet and anti-slip shoes. The channel prunning is carried out on the basis of the original model, and the channel with low weight is deleted to reduce the parameters of the model and improve the detection speed. The experimental results show that the recognition accuracy of the network after channel pruning is 93.2%, which is basically the same as that before channel pruning. Meanwhile, the number of parameters is reduced by 63.9%, and the detection speed is 123.45fps. It provides technical support for the development of embedded detection equipment.