基于RESNET-50算法的军用飞机实时网络检测系统

C. Venkata Sudhakar, Limbakar Deekshitha, Charan Kummari, Rauniyar Pintu Sah, Mahathi Kessamsetty
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引用次数: 0

摘要

由于遥感成像中的目标检测依赖于飞机类型识别,因此在民用和军事应用中都是必不可少的。细粒度特征的存在使这项工作变得更加困难,细粒度特征可能导致由于大小、姿态和角度的变化而导致的重大类内变化,以及由于非常相似的子类别而导致的适度类间变化。这种系统有助于军事安全,因为飞机类型的识别对正在做出的决策非常关键。有几种现有的方法,使用雷达系统和无线电足迹,速度等方法来检测飞机的类型。尽管这些方法非常昂贵,而且仍然不能准确地检测出飞机的类型。本文使用ResNet-50,在Anaconda工具上实现的最先进的目标检测算法对飞机进行检测,训练精度为98%,验证精度为75%。人工智能的一个关键领域是物体检测,它使计算机系统能够通过识别视觉图片或电影中的物体来感知周围环境。在任何危险飞机的情况下,该系统将有能力发出警报和警报使用音频警报器。这个项目的软件要求是python, 3.6/anaconda,或更新的和必要的python模块。
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Real Time Web-based System to Detect Military Aircraft Using RESNET-50 Algorithm
As target detection in remote sensing imaging depends on aircraft type recognition, it is essential in both civil and military applications. The job is made more difficult by the existence of fine-grained features, which can result in significant intra-class changes due to variations in size, posture, and angle, as well as modest inter class changes due to very similar subcategories. This kind of system can be helpful for military security as recognition of the type of aircraft is very critical to the decisions being made. There are several existing ways which uses methods like Radar System and Radio footprints, Speed etc., to detect type of Aircraft. Although these methods are massively costly and still cannot detect the type of Aircraft accurately. In this paper aircraft is detected using ResNet-50, Advance State of Art Object Detection Algorithm implementing in Anaconda tool with train accuracy is 98% & validate accuracy is 75%. A crucial area of artificial intelligence is object detection, which enables computer systems to perceive their surroundings by identifying things in visual pictures or movies. In case of any dangerous Aircraft, the system will have capability to raise alarm and Alert using Audio Sirens. The software requirement for this project is python, 3.6/anaconda, or newer and necessary python modules.
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