用YOLOv8 Nano模型在电梯内精准识别跌倒，提升安全响应速度。
Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
- 采用YOLOv8 Nano模型处理电梯内复杂光照与封闭环境下的跌倒检测。
- 在超1万张图像数据上训练，实现85%精确率与82%召回率。
- 适合电梯安全系统升级，尤其关注老人或行动不便者保护场景。
计算机视觉技术通过深度学习算法分析摄像头捕获的图像与视频，在人体跌倒检测领域取得显著进展。本研究聚焦于在乘客电梯这一特殊环境中应用YOLOv8 Nano模型进行跌倒事件识别，该场景因空间封闭、光照条件多变而具挑战性。基于包含超过10,000张图像、覆盖多种电梯类型的高质量数据集进行训练，旨在提升检测的精度与召回率。模型在实际测试中达到85%的精确率和82%的召回率，展现出其在现有电梯安全系统中集成以实现快速干预的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。