Open Access

ARTICLE

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

by Yanman Li¹, Jun Zhang¹, Yang Hu¹, Yingnan Zhao^2,*, Yi Cao³

1 NARI Nanjing Control System Ltd., Nanjing, 210032, China
2 School of Computer, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 Department of Electrical and computer Engineering, University of Windsor, Windsor, N9B 3P4, Canada

* Corresponding Author: Yingnan Zhao. Email:

Computer Systems Science and Engineering 2022, 43(3), 1219-1230. https://doi.org/10.32604/csse.2022.028224

Received 05 February 2022; Accepted 30 March 2022; Issue published 09 May 2022

Abstract

Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accurate bounding box regression. Third, it utilizes Mish activation function to improve detection accuracy and generalization ability. It builds a safety helmet-wearing detection data set containing more than 10,000 images collected from the Internet for preprocessing. On the self-made helmet wearing test data set, the average accuracy of the helmet detection of the proposed algorithm is 96.7%, which is 1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

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Keywords

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