感兴趣区域YOLO_BFROI的扶梯乘客安全检测算法

doi:10.3778/j.issn.1002-8331.2404-0405

摘要/Abstract

摘要： 自动扶梯智能化监控是预防乘客事故发生的重要手段，然而扶梯运行环境较复杂，背景干扰严重，远距离小目标乘客的检测容易造成漏检和误检问题，提出一种基于感兴趣区域改进YOLOv8的轻量化自动扶梯乘客摔倒检测算法。改进算法设计了基于感兴趣区域的BiFormer_ROI注意力机制模块，构造SPD-Conv和BiFormer_ROI的小目标检测模块组改进YOLOv8骨干网络，屏蔽非扶梯背景区域的复杂环境干扰，有效提高小目标检测率。考虑实际应用需要采用GhostSlimPAFPN轻量化结构优化Neck网络，在保持检测精度的同时有效减少模型参数量。采用具有目标尺寸自适应惩罚因子的PIoU v2损失函数改进Head网络，从而实现更快的收敛和更高的检测精度。在自建扶梯乘客摔倒数据集上，改进算法乘客摔倒平均检测精度达到94.2%，检测帧率为87.7?FPS，检测性能显著提高，能有效减少漏检和误检问题，且具有良好的实时性，可以更好地保障乘客安全乘梯。

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

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, GhostSlimPAFPN 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.

Key words: deep learning, escalator, fall detection, YOLOv8 algorithm, regions of interest, lightweight

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.

HOU Ying, HU Xin, ZHAO Ruirui, ZHANG Nan, XU Yanhong, MA Li. Escalator Passenger Safety Detection YOLO_BFROI Algorithm Based on Region of Interest[J]. Computer Engineering and Applications, 2025, 61(6): 84-95.

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