Safety Helmet Detection Based on Lightweight YOLOv5

doi:10.3969/j.issn.1006-2475.2023.10.009

Abstract

Abstract: There is a large amount of data in the intelligent monitoring system of distribution network， which objectively requires the algorithm to have high real-time performance. Based on this， the YOLOv5 algorithm is improved in light weight， including improving the K-means algorithm clustering anchor box， using the Hard-swish activation function and the CRD loss function， and at the same time integrating the ShuffleNet structure in the backbone network and adopting the Attention mechanism in the FPN module. The presented model， SNAM-YOLOv5 （ShuffleNet and Attention Mechanism-You Only Look Once version 5）， can significantly improve the detection performance and the processing speed of small targets and occluded targets. The results of safety helmet detection based on HiSilicon Hi3559A embedded platform show that the model is superior to similar algorithms and has good real-time performance.

Key words: Key words: deep learning, distribution network operation and maintenance, construction safety, intelligent monitoring, lightweight network, safety helmet detection

CLC Number:

TP391.4

LI Yan-man, WANG Bi-heng, ZHAO Ling-yan. Safety Helmet Detection Based on Lightweight YOLOv5[J]. Computer and Modernization, 2023, 0(10): 59-64.

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