基于改进YOLOv8n的施工现场安全帽检测算法
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山东理工大学计算机与技术学院 淄博 255022

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TN919.8

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国家自然科学基金(62076152)、淄博市科技型中小企业创新能力提升工程项目(2023tsgc0043)、张店区校城融合发展计划项目(2021JSCG0018)资助


A construction site safety helmet hetection algorithm based on improved YOLOv8n
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College of Computer Science and Technology, Shandong University of Technology,Zibo 255022, China

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    摘要:

    建筑、采矿、勘探等施工现场是非常复杂且多样化的区域,在这类场景下进行安全帽佩戴检测时,会存在图像遮挡严重、小目标信息容易丢失的问题。为此本文提出了一种基于改进YOLOv8n的安全帽佩戴检测算法。首先,对YOLOv8n模型的C2f模块进行改进,融入改进后的倒置残差块注意力机制,使模型能够高效捕获全局特征,充分利用安全帽特征的关键信息;其次,结合SPPF模块和LSKA注意力机制,提出了SPPF-LSKA模块,提升网络对安全帽关键信息的关注度,避免实际复杂场景中背景信息对安全帽佩戴状态检测的影响;最后,使用Inner-SIoU损失函数优化网络模型,提升模型对安全帽佩戴状态检测的稳定性。实验结果表明,最终本文算法在复杂环境下安全帽佩戴状态检测的mAP@0.5达到了93.7%,较原YOLOv8算法的P、R、mAP@0.5和mAP@0.5:0.95分别提高了2.4%、4.0%、3.4%和5.3%,参数量降低了3.5%,计算量降低了5.9%,改善了安全帽佩戴状态检测误检和漏检的状况,便于实际检测应用的部署。

    Abstract:

    Construction sites such as construction, mining, and exploration are very complex and diverse areas. When conducting helmet wearing detection in such scenarios, there are problems such as severe image occlusion and easy loss of small target information. This article proposes a helmet wearing detection algorithm based on improved YOLOv8n. Firstly, the C2f module of the YOLOv8n model is improved by incorporating an improved inverted residual block attention mechanism, enabling the model to efficiently capture global features and fully utilize the key information of safety helmet features; secondly, by combining the SPPF module and LSKA attention mechanism, the SPPFLSKA module is proposed to enhance the network′s attention to key information of safety helmets and avoid the influence of background information on the detection of safety helmet wearing status in practical complex scenarios; finally, the Inner-SIoU loss function is used to optimize the network model and improve the stability of the model in detecting the wearing status of safety helmets. The experimental results show that the algorithm proposed in this paper can effectively detect the wearing status of helmets in complex environments mAP@0.5 has reached 93.7%, compared to the original YOLOv8 algorithm′s P, R, mAP@0.5 and mAP@0.5:0.95 has increased by 2.4%, 4.0%, 3.4%, and 5.3% respectively, the number of parameters has decreased by 6.7%, and the computational workload has decreased by 4.8%, improving the detection of false and missed safety helmet wearing status, facilitating the deployment of practical detection applications.

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齐瑞洁,袁玉英,孙立云,乔世超.基于改进YOLOv8n的施工现场安全帽检测算法[J].电子测量技术,2024,47(13):100-109

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  • 在线发布日期: 2024-11-07
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