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Open Access

ARTICLE

An Improved Lightweight Safety Helmet Detection Algorithm for YOLOv8

Lieping Zhang1,2, Hao Ma1, Jiancheng Huang3, Cui Zhang4,*, Xiaolin Gao2
1 Key Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin University of Technology, Guilin, 541004, China
2 Guangxi Key Laboratory of Special Engineering Equipment and Control, Guilin University of Aerospace Technology, Guilin, 541004, China
3 Guangxi Tianli Construction Engineering Co., Ltd., Guilin, 541001, China
4 School of Information Engineering, Nanning College of Technology, Guilin, 541004, China
* Corresponding Author: Cui Zhang. Email: email
(This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.061519

Received 26 November 2024; Accepted 23 January 2025; Published online 20 February 2025

Abstract

Detecting individuals wearing safety helmets in complex environments faces several challenges. These factors include limited detection accuracy and frequent missed or false detections. Additionally, existing algorithms often have excessive parameter counts, complex network structures, and high computational demands. These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems. Aiming at this problem, this research proposes an optimized and lightweight solution called FGP-YOLOv8, an improved version of YOLOv8n. The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added. This modification minimizes computational costs with only a minor impact on accuracy. A new GSTA (GSConv-Triplet Attention) module is introduced to enhance feature fusion and reduce computational complexity. This is achieved using attention weights generated from dimensional interactions within the feature map. Additionally, the ParNet-C2f module replaces the original C2f (CSP Bottleneck with 2 Convolutions) module, improving feature extraction for safety helmets of various shapes and sizes. The CIoU (Complete-IoU) is replaced with the WIoU (Wise-IoU) to boost performance further, enhancing detection accuracy and generalization capabilities. Experimental results validate the improvements. The proposed model reduces the parameter count by 19.9% and the computational load by 18.5%. At the same time, mAP (mean average precision) increases by 2.3%, and precision improves by 1.2%. These results demonstrate the model’s robust performance in detecting safety helmets across diverse environments.

Keywords

YOLO; safety helmet detection; complex environments; lightweight; WIoU loss function

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