为提高传统目标检测的识别效果和准确率,并加快运算速度,提出了一种具有更强大特征学习和特征表达能力的卷积神经网络(CNN:Convolutional Neural Network)模型和相关的深度学习训练算法,并将其应用于计算机视觉领域的大规模识别任务.首先详细分析了传统目标检测算法,如V-J(Viola-Jones)检测器、HOG(Histogram of Oriented Gradients)特征结合 SVM(Support Vector Machine)分类器和 DPM(Deformable Parts Model)检测器的特点.然后提出了深度学习算法,如RCNN(Region-based Convolutional Neural Networks)算法和YOLO(You Only Look Once)算法,并分析了其在目标检测任务中的应用现状.针对倒地检测任务,使用YOLOv5(You Only Look Once version 5)模型对不同身高体型目标人群的行为进行训练.通过使用不同的交并比(IOU:Intersection over Union)、准确率(Precision,P)、召回率(Recall,R)和 PR 曲线等评估指标,对 YOLOv5 模型进行了分析,评估了其在检测站立和倒地两种活动方式的实际效果.同时通过预训练和增强处理,增加了训练样本数量并提高了网络的识别准确率.实验结果表明,倒地识别率达到了 86％.可将其应用于灾区探测救援类机器人的设计中,以辅助识别和分类受伤倒地人员,提高灾区救援效率.
Fall Detection Based on YOLOv5
In order to improve the recognition performance and accuracy of traditional object detection and to accelerate the computation speed,a CNN(Convolutional Neural Network)model with more powerful feature learning and representation capabilities and with related deep learning training algorithms is adopted and applied to large-scale recognition tasks in the field of computer vision.The characteristics of traditional object detection algorithms,such as the V-J(Viola-Jones)detector,HOG(Histogram of Oriented Gradients)features combined with SVM(Support Vector Machine)classifier,and DPM(Deformable Parts Model)detector are analyzed.Subsequently,the deep learning algorithms that emerged after 2013,such as the RCNN(Region-based Convolutional Neural Networks)algorithm and YOLO(You Only Look Once)algorithm are introduced,and their application status in object detection tasks is analyzed.To detect fallen individuals,the YOLOv5(You Only Look Once version 5)model is used to train the behavior of individuals with different heights and body types.By using evaluation metrics such as IoU(Intersection over Union),Precision,Recall,and PR curves,the YOLOv5 model is analyzed and evaluated for its performance in detecting both standing and fallen activities.In addition,by pre-training and data augmentation,the number of training samples is increased,and the recognition accuracy of the network is improved.The experimental results show that the recognition rate of fallen individuals reaches 86％.The achievements of this study will be applied to the design of disaster detection and rescue robots,assisting in the identification and classification of injured individuals who have fallen,and improving the efficiency of disaster area rescue.
target detectionconvolutional neural networkyou only look once modelcomputer visiondeep learning
国家科技期刊平台
NSTL
万方数据
维普
