计算机工程2024，Vol.50Issue(3)：277-289,13.DOI:10.19678/j.issn.1000-3428.0067416
基于SwinT-YOLOX模型的自动扶梯行人安全检测算法
Automatic Escalator Pedestrian Safety Detection Algorithm Based on SwinT-YOLOX Model
摘要
Abstract
Escalators are widely used in public places.If passenger fall accidents cannot be detected and handled in a timely manner,they will cause serious personal injury.Therefore,it is imperative to achieve intelligent monitoring and management of escalators.Owing to the complex operating environment,large number of pedestrians,and local occlusion of escalators,traditional human posture feature fall detection models have poor performance and slow detection speed.A pedestrian fall detection algorithm for escalators is proposed based on the SwinT-YOLOX network model,which combines the excellent strategy of the Swin Transformer and YOLOX object detection algorithms.Adopting the Swin Transformer model as the backbone network,the neck network uses the YOLOX model with an added attention mechanism to further enhance the diversity and expression ability of feature maps.In addition,utilizing the Funnel Rectified Linear Unit(FReLU)visual activation function to construct a CBF module improves the structure of the neck and Head networks,thereby achieving better feature detection performance.The experimental results demonstrate that compared with algorithms such as AlphaPose,OpenPose,and YOLOv5,the detection performance of this algorithm is significantly improved for self-built escalator pedestrian fall databases and network collection of actual escalator pedestrian fall accidents.The average detection accuracy of pedestrian falls can reach 95.92%,with a detection frame rate of 24.08 frames/s,which can quickly and accurately detect the occurrence of passenger fall accidents.The monitoring management platform immediately takes safety emergency stop measures to ensure passenger safety.关键词
自动扶梯/摔倒检测/深度学习/YOLOX模型/Swin Transformer模型/漏斗修正线性单元视觉激活函数Key words
automatic escalator/fall detection/deep learning/YOLOX model/Swin Transformer model/Funnel Rectified Linear Unit(FReLU)visual activation function分类
信息技术与安全科学引用本文复制引用
侯颖,杨林,胡鑫,贺顺,宋婉莹,赵谦..基于SwinT-YOLOX模型的自动扶梯行人安全检测算法[J].计算机工程,2024,50(3):277-289,13.基金项目
国家自然科学基金(62071481,61901358) （62071481,61901358）
陕西省科技厅工业攻关项目(2022GY-115). （2022GY-115）
