In the 21st century, with the rapid development of high technology, artificial intelligence technology is becoming more and more mature, and its application in various aspects is becoming more and more extensive. Image recognition is an important feature of artificial intelligence. For mobile target image detection and application, the reasonable application of artificial intelligence technology and related research analyzes the changes brought by mobile target image detection and application and adopts its optimal scheme which can improve the innovation of mobile target image detection and application. Moving object image detection and tracking are the algorithm process of intelligent dynamic analysis. It also provides a lot of help for the development of many research aspects. This paper analyzes and studies the detection and application of mobile target images from the perspective of artificial intelligence and improves the algorithm of the current target image detection. The improved artificial intelligence algorithm will be applied to future mobile target image detection and application. The results show that in the nonoptimized detection and reconnaissance system of moving targets, the program can run in parallel after the system is optimized, and the running time of each image is kept between 5 and 8 seconds, thus improving the system’s performance. Through the continuous trend of linear graphics, the number of actual test frames increases, the line graph tends to be stable, and the performance is improved by about 20–30 times, which shows the feasibility of the algorithm.

中文翻译：

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基于人工智能的移动目标图像检测与应用

进入21世纪，随着高新技术的飞速发展，人工智能技术日趋成熟，其在各个方面的应用也越来越广泛。图像识别是人工智能的一个重要特征。针对移动目标图像检测与应用，人工智能技术的合理应用及相关研究分析了移动目标图像检测与应用带来的变化，并采用其优化方案，提高了移动目标图像检测与应用的创新性。运动物体图像检测与跟踪是智能动态分析的算法过程。也为许多研究方面的发展提供了很大的帮助。本文从人工智能的角度分析研究了移动目标图像的检测与应用，改进了当前目标图像检测的算法。改进后的人工智能算法将应用于未来的移动目标图像检测和应用。结果表明，在未优化的运动目标检测侦察系统中，系统优化后程序可以并行运行，每幅图像的运行时间保持在5~8秒之间，从而提高了系统的性能。通过线形图的连续趋势，实际测试帧数增加，线形图趋于稳定，性能提升约20-30倍，可见该算法的可行性。