Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction

Author:

Han Ju1,Liu Yicheng2,Li Zhipeng2,Liu Yan2,Zhan Bixiong1

Affiliation:

1. China Construction First Group Construction & Development Co., Ltd., Beijing 100102, China

2. College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Abstract

High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries.

Funder

research on automatic and intelligent safety management technology at construction sites

research on intelligent construction site management based on Internet of Things and image recognition technology

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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3. Detection of Safety Helmets Using YOLOv5s Model;2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS);2024-07-10

4. MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations;Applied Sciences;2024-05-31

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