通过增强训练数据来提高深度学习性能

Soldatenko Dmytro, Hnatushenko Viktorija
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引用次数: 0

摘要

卫星图像识别是计算机视觉的一个重要应用,在灾害管理、农业和城市规划等各个领域都有应用潜力。本研究的目的是确定所需的最佳输入数据量,并选择训练用于卫星图像识别的卷积神经网络(CNN)所需的最有效的增强方法。为了实现这一点,我们执行了一系列实验来研究输入数据量对几个性能指标的影响,包括模型精度、收敛性和泛化。此外,我们还探讨了各种数据增强技术(如旋转、缩放和翻转)对模型性能的影响。该研究提出了几种识别饱和点和减轻过度训练影响的策略,包括早期停止和退出正则化。本研究的发现可以为开发更高效的卫星识别模型做出重大贡献。此外,除了为未来的研究提供指导外,它们还可以帮助改进现有模型的性能。该研究强调了仔细选择输入数据和增强方法以实现cnn最佳性能的重要性,这是推进计算机视觉领域的基础。除此之外,该研究还通过在相关数据集上预训练模型并在卫星图像数据集上对其进行微调来研究迁移学习的潜力。这种方法可以减少所需的数据量和训练时间,提高模型性能。总的来说,本研究为训练cnn用于卫星图像识别的最佳输入数据量和增强技术提供了有价值的见解,其研究结果可以指导该领域的未来研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving deep learning performance by augmenting training data
Satellite image recognition is a crucial application of computer vision that has the po-tential to be applied in various fields such as disaster management, agriculture, and urban planning. The objective of this study is to determine the optimal amount of input data required and select the most effective methods of augmentation necessary for training a convolutional neural network (CNN) for satellite image recognition. To achieve this, we perform a series of experiments to investigate the effect of input data quantity on several performance metrics, including model accuracy, convergence, and generalization. Additionally, we explore the impact of various data augmentation techniques, such as rotation, scaling, and flipping, on model performance. The study suggests several strategies for identifying the saturation point and mitigating the effects of overtraining, in-cluding early stopping and dropout regularization. The findings from this study can significantly contribute to the development of more ef-ficient satellite recognition models. Furthermore, they can help improve the performance of existing models, in addition to providing guidance for future research. The study emphasizes the importance of carefully selecting input data and augmentation methods to achieve optimal performance in CNNs, which is fundamental in advancing the field of computer vision. In addition to the above, the study investigates the potential of transfer learning by pre-training the model on a related dataset and fine-tuning it on the satellite imagery dataset. This approach can reduce the amount of required data and training time and increase model performance. Overall, this study provides valuable insights into the optimal amount of input data and augmentation techniques for training CNNs for satellite image recognition, and its findings can guide future research in this area.
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