Algorithm optimization scheme of machine vision technology in automatic anomaly detection system
机器视觉技术在自动化异常检测系统中的算法优化方案

摘要

机器视觉技术在自动化异常检测系统中发挥着重要作用，但现有算法在检测精度和实时性方面仍存在不足。本文提出一种基于深度学习的优化方案，通过改进卷积神经网络结构、引入注意力机制以及优化数据增强策略，显著提升了系统的检测性能。实验结果表明，优化后的算法在检测精度和速度上均优于传统方法，为自动化异常检测系统的实际应用提供了有力支持。

Abstract

Machine vision technology plays a crucial role in automated anomaly detection systems, but existing algorithms still fall short in terms of detection accuracy and real-time performance. This paper proposes an optimized solution based on deep learning, which significantly enhances the system's detection performance by improving the convolutional neural network structure, introducing attention mechanisms, and optimizing data augmentation strategies. Experimental results show that the optimized algorithm outperforms traditional methods in both detection accuracy and speed, providing strong support for the practical application of automated anomaly detection systems.