计算机工程与应用2025，Vol.61Issue(6)：84-95,12.DOI:10.3778/j.issn.1002-8331.2404-0405
感兴趣区域YOLO_BFROI的扶梯乘客安全检测算法
Escalator Passenger Safety Detection YOLO_BFROI Algorithm Based on Region of Interest
摘要
Abstract
Intelligent monitoring of escalators is an important means of preventing passenger accidents.However,the operating environment of escalators is complex with small target passenger detection,which can easily lead to missed and false detection.Therefore,a region of interest-based escalator passenger fall detection algorithm using the improved YOLOv8 is proposed in this paper.Firstly,the BiFormer_ROI attention mechanism module based on regions of interest is designed,and a small object detection module group of SPD-Conv and BiFormer_ROI is constructed to improve the YOLOv8 backbone network,so as to shield the complex environmental interference of non-escalator background areas and effectively improve the small targets detection rate.Secondly,considering the practical industrial applications,Ghost-SlimPAFPN lightweight structure is adopted to optimize the Neck network,which effectively reduces the number of model parameters while maintaining detection accuracy.Finally,the PIoU v2 loss function with target size adaptive penalty factor is adopted to improve the Head network,thereby achieving faster convergence and higher detection precision.On the self-built escalator passenger fall dataset,the experimental results show that the improved algorithm achieves 94.2%average detection precision and 87.7 FPS detection speed.It can effectively reduce false and missed detection,which can better ensure the safety of passengers on the elevator.关键词
深度学习/自动扶梯/摔倒检测/YOLOv8算法/感兴趣区域/轻量化Key words
deep learning/escalator/fall detection/YOLOv8 algorithm/regions of interest/lightweight分类
计算机与自动化引用本文复制引用
侯颖,胡鑫,赵瑞瑞,张楠,徐艳红,马莉..感兴趣区域YOLO_BFROI的扶梯乘客安全检测算法[J].计算机工程与应用,2025,61(6):84-95,12.基金项目
国家自然科学基金(61901357) （61901357）
陕西省重点研发计划(2021ZDLGY07-08). （2021ZDLGY07-08）
