计算机与现代化Issue(10)：1-6,6.DOI:10.3969/j.issn.1006-2475.2025.10.001
基于深度学习的厂区人员异常行为识别轻量化模型
Lightweight Model for Recognizing Abnormal Behavior in Factory Personnel Based on Deep Learning
摘要
Abstract
This paper proposes an improved lightweight network for recognizing abnormal behavior among factory personnel based on YOLOv5,to addressing challenges such as complex backgrounds and limited computational resources.This network in-tegrates Omni-dimensional Dynamic Convolution(ODConv)and the Explicit Visual Center Block(EVCBlock),resulting in im-proved detection performance while reducing parameter computation.The ODConv module is introduced in the neck network to enhance the model's adaptability to complex factory environments and decrease the number of model parameters,while the EVC-Block module is added at the end of the backbone network to improve the detection accuracy of the model and compensate for ac-curacy loss of model caused by the reduction of parameters.The Normalized Wasserstein Distance(NWD)loss is constructed to optimize the model training process and enhance the model's detection performance on small targets.Several enhanced detection models are constructed based on existing lightweight methods to compare detection accuracy and parameter count.Results demon-strate that the proposed lightweight recognition model has fewer parameters while maintaining high detection accuracy compared with the existing methods.Compared with the original model,the mAP of the detection model built in this paper increases by 3.2 percentage points and GFLOPs decreases by 2.2.This work is of guiding significance to realize rapid detection and accurate iden-tification of factory personnel's abnormal behavior in industrial production scenarios.关键词
轻量级识别模型/异常行为识别/YOLOv5/全维动态卷积/显式视觉中心Key words
lightweight model/abnormal behavior detection/YOLOv5/omni-dimensional dynamic convolution/explicit visual center分类
计算机与自动化引用本文复制引用
刘龙恩,石东祥,单宝明,张方坤,徐啟蕾..基于深度学习的厂区人员异常行为识别轻量化模型[J].计算机与现代化,2025,(10):1-6,6.基金项目
国家自然科学基金资助项目(62103216) （62103216）
