计算机与现代化Issue(8)：43-48,113,7.DOI:10.3969/j.issn.1006-2475.2024.08.008
面向智慧运维系统的改进YOLOv8行为检测算法
Improved YOLOv8 Behavior Detection Algorithm for Intelligent Operation and Maintenance System
摘要
Abstract
Aiming at the problem that the intelligent operation and maintenance system is difficult to stably detect the behavior of computer room staff when maintaining the security of the computer room,leading to potential safety hazards,an improved YOLOv8 behavior detection algorithm is proposed.Firstly,an adaptive spatial weight convolution module is designed to improve the original C2f module and enhance the network's ability to acquire multi-scale features.Secondly,a multi-residual deform-able convolution module is proposed to enhance the algorithm's ability to learn irregular spatial features,and it is integrated into the neck network to further improve the detection accuracy of computer room staff behavior.Then,aiming at the problem of the lack of current computer room image datasets,relevant images are collected and labeled from existing media,and transfer learn-ing is used to further debug and optimize based on existing training weights.Finally,the Wise-IoU loss function is introduced to solve the impact of low-quality examples in the self-built dataset on training results.Experiment results show that the improved algorithm achieves a test accuracy of 87.84%on the standard NTU RGB+D dataset,which is superior to the comparison algo-rithm;compared with the original YOLOv8 in real computer room tests,the accuracy and recall rate are improved by 13.24%and 10.47%,respectively,and the parameter quantity is reduced by 18.07%.关键词
智慧运维/机房安全/行为检测/YOLOv8/可变形卷积/迁移学习Key words
intelligent operation and maintenance/computer room security/behavior detection/YOLOv8/deformable convolu-tion/transfer learning分类
信息技术与安全科学引用本文复制引用
马永,王俊,张子健,赵煜阳,张靖,周明..面向智慧运维系统的改进YOLOv8行为检测算法[J].计算机与现代化,2024,(8):43-48,113,7.基金项目
安徽省高等学校自然科学基金重点项目(KJ2020A0250) （KJ2020A0250）
