使用智能监控摄像头系统检测乘客电梯中的跌倒：一项基于 YoloV8 Nano 模型的应用
Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
AI总结：
本文将 YoloV8 Nano 用于乘客电梯跌倒检测，基于逾万张多类型电梯图像训练，达到 85% 精确率和 82% 召回率，可支持电梯安全系统快速干预。
摘要：
计算机视觉技术通过深度学习算法分析摄像头采集的图像和视频，已显著推动了人体跌倒检测领域的发展。本研究聚焦于 YoloV8 Nano 模型在识别乘客电梯内跌倒事件方面的应用；由于环境封闭且光照条件多变，这一场景面临独特挑战。通过在包含不同电梯类型、超过 10,000 张图像的稳健数据集上训练该模型，我们旨在提高检测精确率和召回率。该模型在跌倒检测中取得了 85% 的精确率和 82% 的召回率，其性能凸显了将其集成到现有电梯安全系统中以实现快速干预的潜力。
英文摘要：
Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context that presents unique challenges due to the enclosed environment and varying lighting conditions. By training the model on a robust dataset comprising over 10,000 images across diverse elevator types, we aim to enhance the detection precision and recall rates. The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems to enable rapid intervention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
