|Table of Contents|

Safety Helmet Detection Algorithm Based on Improved YOLOX_s for Complex Scenes(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年02期
Page:
107-114
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Safety Helmet Detection Algorithm Based on Improved YOLOX_s for Complex Scenes
Author(s):
Jiang Xinling1; Yang Le1; Zhu Jiahui1; Tao Lei1; Liu Feng1; 2; Duan Qianqian1
(1.College of Information and Computer,Taiyuan University of Technology,Jinzhong 030600,China)
(2.China National Coal Association,Beijing 100010,China)
Keywords:
safety helmet detection;  improved YOLOX_s;  occlusion targets;  small targets;  dense targets
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.02.014
Abstract:
In the process of industrial production, safety helmet is an important safety protection tool for production workers. Aiming at the problems of missing detection and low detection accuracy of the existing safety helmet detection algorithm for small targets, dense targets and occlusion targets in complex application scenarios, an improved algorithm based on YOLOX_s is proposed. Firstly, by improving the model structure of YOLOX_s algorithm, a new prediction feature layer with a size of 160×160 is established on the basis of the original network structure, the prediction feature layer can predict small targets by effectively fusing high-level semantic information and low-level location information. Secondly, for the complex helmet detection environment, BCE_Loss of obj_loss is changed to Focal_Loss, that is, Focal_Loss is used to train obj branch to reduce missed detection. Finally, residual block is changed in CSP1_X to the shuffleNet base unit to reduce parameters number, mAP and recall of the improved algorithm are increased by 1.25% and 2.32% respectively, and parameters number is reduced to 3.61MB. The improved algorithm effectively reduces the missed detection rate and improves the detection accuracy of safety helmet in complex environment, and plays a certain role in the actual production process to ensure the property and life safety of enterprises and workers.

References:

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Last Update: 2023-06-15