基于深度学习的摄像头入侵检测技术研究

Research on Camera Intrusion Detection Technology Based on Deep Learning

当下人工智能在世界上的影响力日益增加,在工作和生活的各个方面,人工智能都在不同程度上扮演着重要的角色。它改变了我们的工作方式和生活节奏,同时提升了生活品质。其中机器视觉技术是依托人工智能技术迅速发展的领域之一,其应用范围和功能都在拓展。在人类生产和认知行为中,机器视觉技术起到了至关重要的作用。在众多应用领域中,人形检测技术是其中较为关键的一项。研究的主要对象是深度学习目标检测在实际应用中的场景,旨在设计一款基于深度学习的摄像头入侵检测系统[1-3]。该系统采用了轻量级的人工智能算法yolov5[4]进行模型训练,并通过裁剪、数据增强等数据优化技术,提升软件性能。

The influence of artificial intelligence in the world is increasing.Whether in work or various aspects of life,AI plays an important role in different degrees.It has changed our working methods and lifestyle rhythms,while also enhancing portability and quality of life.Among them,machine vision technology is a rapidly developing field based on artificial intelligence.Its application scope and functionalitie continue to expand.Machine vision technology plays a crucial role in human production and cognitive behaviors.Among the numerous application areas,human detection technology is one of the most critical.This study focuses on the practical application scenarios of deep learning-based object detection,aiming to design and implement a camera intrusion detection system based on deep learning[1-3].The system utilizes the lightweight artificial intelligence algorithm YOLOv5 for model training.Optimization techniques such as model pruning,model fusion,model compression,and data augmentation are employed to meet the requirements of real-time multi-thread processing and enhance software performance.