基于改进YOLOv5s的着装不规范检测算法研究

Detection of dress code violations based on improved YOLOv5s

针对餐饮后厨工作人员着装不规范,在复杂背景下采用现有算法检测精度低且易出现误检、漏检等问题,提出一种基于YOLOv5s的着装规范检测改进算法YOLOv5s-ESW。首先,在主干网络引入新型多尺度注意力机制改进C3模块,增强网络的特征提取能力;其次,在颈部网络中采用空间和通道重建卷积模块(SCConv)替换原始网络中的卷积模块(Conv),减少模型参数冗余,同时提升模型的精度;最后,在预测部分引入WIoU损失函数更换CIoU损失函数,提高模型泛化能力,加快收敛速度。将改进算法应用到自建餐饮后厨工作人员着装数据集中进行实验,实验表明,改进后的模型检测平均精度提升了4.1%,参数量减少了11.4%。该模型在提高了检测精度的同时降低了网络复杂度,能够满足餐饮后厨工作人员的着装规范检测的要求。

Addressing the issue of non-compliance in the attire of culinary staff in the complex background of the catering kitchen,where existing algorithms tend to have low detection accuracy and are prone to false detections and omissions,this paper proposed an improved attire compliance detection algorithm,YOLOv5s-ESW,based on YOLOv5s.Firstly,a novel multi-scale attention mechanism was introduced into the main network to enhance the network’s feature extraction capability.Secondly,within the neck network,the spatial and channel reconstruction convolution module(SCConv)replaced the original convolution module(Conv)to reduce model parameter redundancy and simultaneously enhanced model accuracy.Lastly,the WIoU loss function was introduced in the prediction part to accelerate convergence and enhance the model’s generalization capability.The improved algorithm was applied to a self-compiled dataset of catering kitchen staff attire for experimentation.The results validated that the improved model has elevated its mean detection accuracy by 4.1%and reduced its parameter quantity by 11.4%.While enhancing detection accuracy,the model also reduced network complexity,thereby satisfying the requirements for attire compliance detection among catering kitchen staff.