基于物联网的蚂蚁算法在图像识别系统中的应用

Hang Yu, Yujie Wang, Jiajia Song
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引用次数: 0

摘要

随着社会的发展,人们对生活质量的要求越来越高,互联网已经成为我们日常生活中不可缺少的一部分。蚂蚁很典型,很常见,很方便,很有创意,很方便。本文主要介绍了基于物联网和蚁群计算的方法。通过分析国内外蚂蚁算法的研究现状及相关文献,得出结论,并提出改进方案,完善该领域的理论体系,进一步优化物联网环境下医疗设备和药材图像识别应用。然后,根据本文要实现的系统功能,确定目标函数,设计指标和参数,提取特征。最后,我们得到特征向量的最优解,然后将数据发送到后台数据库,得到识别结果,并在实验中验证模型。本文设计了一个简单、低成本、高效、高精度的基于蚂蚁算法的识别系统进行测试。通过对物联网环境下医疗设备和原料药的图像识别实验,基于蚂蚁算法的图像识别系统实现了特征提取时间在20秒以内,同时驱动系统识别时间达到26秒,特征匹配率达到82%以上,完全可以满足用户的图像识别需求,该方案不仅节省了资源,而且具有较高的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ant Algorithm Based on Internet of Things in Image Recognition System
With the growth of society, people have higher and higher requirements for the quality of life, and the Internet has become an indispensable part of our daily life. Ants are very typical, common, convenient, creative and convenient. This paper mainly introduces the methods based on the Internet of Things and ant colony computing. By analyzing the research status of ant algorithm at home and abroad and relevant literature, we draw conclusions, and propose improvement plans to improve the theoretical system in this field, further optimize the image recognition application of medical equipments and medicine materials in the Internet of Things environment. Then, according to the system functions to be achieved in this paper, we determine the objective function, design indicators and parameters, so as to extract features. Finally, we get the optimal solution of the feature vector, and then send the data to the background database to obtain the recognition results, And verify the model in the experiment This paper designs a simple, low-cost, efficient and high-precision recognition system based on the ant algorithm to test. Through the image recognition experiment for medical equipments and drug materials in the Internet of Things environment, the image recognition system based on the ant algorithm achieves the feature extraction time within 20 seconds, while driving the system recognition time to reach 26 seconds, with a feature matching rate of more than 82%, which can fully meet the user’s image recognition needs, The scheme not only saves resources, but also has high practical value.
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