基于DCA-Net深度学习模型的人体红外图像识别方法

IF 1 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal on Artificial Intelligence Tools Pub Date : 2022-11-23 DOI:10.1142/s0218213023600047

Huiqiang Zhang, Ji Li, Shengqi Liu, Wei Wang

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引用次数: 0

摘要

随着煤炭资源的不断开发，开采过程中的人身安全受到了严重威胁。因此，建立高效的人体红外图像识别系统具有重要意义。本文采用热像仪采集人体红外图像数据，分为三类:human、human others和None。根据井下红外图像的特点，设计了分布式通道特征提取模块(DCFE)，并在此基础上提出了DCA-Net。实验结果表明，该网络的识别率达到98%。与其他网络相比，该网络具有更好的识别性能。其中，DCA-Net50的识别率达到98.214%，参数量和计算量相对较少，性价比最高。适用于对精度要求高、实时性要求高的人体红外图像识别系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Human Body Infrared Image Recognition Approach via DCA-Net Deep Learning Models

With the continuous exploitation of coal resources, human safety has been seriously threatened during the mining process. Therefore, it is of great significance to establish an efficient human infrared image recognition system. In this paper, three classes of infrared image data of the human body are collected by a thermal imager, namely Human, Human others and None. According to the characteristics of downhole infrared images, a distributed channel feature extraction module (DCFE) is designed, and DCA-Net is proposed based on this module. The experimental results show that the recognition rate of the network reaches 98%. Compared with other networks, this network has better recognition performance. Among them, the recognition rate of DCA-Net50 reaches 98.214%, the amount of parameters and calculations are relatively small, and the cost-effectiveness is the highest. It is suitable for the human body infrared image recognition system that requires high accuracy and high real-time performance.

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来源期刊

International Journal on Artificial Intelligence Tools 工程技术-计算机：跨学科应用

CiteScore

2.10

自引率

9.10%

发文量

审稿时长

8.5 months

期刊介绍： The International Journal on Artificial Intelligence Tools (IJAIT) provides an interdisciplinary forum in which AI scientists and professionals can share their research results and report new advances on AI tools or tools that use AI. Tools refer to architectures, languages or algorithms, which constitute the means connecting theory with applications. So, IJAIT is a medium for promoting general and/or special purpose tools, which are very important for the evolution of science and manipulation of knowledge. IJAIT can also be used as a test ground for new AI tools. Topics covered by IJAIT include but are not limited to: AI in Bioinformatics, AI for Service Engineering, AI for Software Engineering, AI for Ubiquitous Computing, AI for Web Intelligence Applications, AI Parallel Processing Tools (hardware/software), AI Programming Languages, AI Tools for CAD and VLSI Analysis/Design/Testing, AI Tools for Computer Vision and Speech Understanding, AI Tools for Multimedia, Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Image Understanding, Integrated/Hybrid AI Approaches, Intelligent System Architectures, Knowledge-Based/Expert Systems, Knowledge Management and Processing Tools, Knowledge Representation Languages, Natural Language Understanding, Neural Networks for AI, Object-Oriented Programming for AI, Reasoning and Evolution of Knowledge Bases, Self-Healing and Autonomous Systems, and Software Engineering for AI.