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 are-as,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 train-ing.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.
deep learningyolov5convolutional neural networkobject detection
维普
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