Fighter Aircraft Detection using CNN and Transfer Learning

Motati Dinesh Reddy, Sai Venkata Rao Kora, Gnana Samhitha Ch
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Abstract

In this work, Deep learning techniques such as Convolutional Neural networks (CNN) and Transfer Learning are used to detect and identify Fighter aircraft or jets. A dataset consisting of 21 different aircraft with 20000 images is being processed using the above algorithms. CNN works on the principle of "pooling," which progressively reduces the spatial size of the model to decrease the number of parameters and computations in the network. CNN's are widely used for image detection in different domains, including defense, agriculture, business, face recognition technology, etc. Transfer learning is a machine learning method where a model created for a task is reused as the initial point for a model on a second task. Transfer learning is related to issues such as multi-task learning and concept drift and is not only an area of study in deep learning. The dataset is processed and uses python libraries such as pandas, seaborn, sci-kit- learn, etc., to find any pre-trained patterns and insights. Data is separated into train and test datasets with 80-20 percent of total data, respectively. A model is built using the TensorFlow library for CNN. The metric used is "accuracy." A transfer learning model is also built to compare the accuracy results and adopt the best-fitting one
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使用CNN和迁移学习的战斗机检测
在这项工作中,使用卷积神经网络(CNN)和迁移学习等深度学习技术来检测和识别战斗机或喷气式飞机。使用上述算法处理由21架不同飞机和20000张图像组成的数据集。CNN的工作原理是“池化”,即逐步减小模型的空间大小,以减少网络中参数和计算的数量。CNN被广泛用于不同领域的图像检测,包括国防、农业、商业、人脸识别技术等。迁移学习是一种机器学习方法,其中为任务创建的模型被重用为第二个任务的模型的初始点。迁移学习涉及到多任务学习和概念漂移等问题,它不仅仅是深度学习的一个研究领域。数据集经过处理并使用python库(如pandas, seaborn, sci-kit- learn等)来查找任何预训练的模式和见解。数据被分为训练数据集和测试数据集,分别占总数据的80- 20%。使用TensorFlow库为CNN构建模型。使用的度量标准是“准确性”。建立迁移学习模型,比较准确率结果,并采用最优拟合结果
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