使用监督深度学习的未爆弹药自动视觉识别

Georgios Begkas, Panagiotis Giannakeris, K. Ioannidis, Georgios Kalpakis, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris
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引用次数: 0

摘要

未爆弹药(UXO)分类是一项具有挑战性的任务,目前使用昂贵的电磁感应装置来解决,并且可能需要在潜在危险的环境中实际存在。迄今为止,开放的未爆炸弹药数据有限,阻碍了基于图像的未爆炸弹药分类的进展,这可能以较低的成本提供一种安全的替代办法。此外,现有的零星努力主要集中在小规模实验上,只使用常见未爆弹药类别的一个子集。我们的工作旨在激发基于图像的未爆弹药分类的研究兴趣,管理一个新的数据集,该数据集由来自8个主要未爆弹药类别的10000多张带注释的图像组成。通过对监督深度学习的广泛实验,我们发现了这项任务中具有挑战性方面的关键见解。最后,我们通过训练最先进的卷积神经网络和视觉转换器,在我们的新基准上设置基线,该转换器能够以84.33%的准确率区分高度重叠的未爆炸弹药类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Visual Recognition of Unexploded Ordnances Using Supervised Deep Learning
Unexploded Ordnance (UXO) classification is a challenging task which is currently tackled using electromagnetic induction devices that are expensive and may require physical presence in potentially hazardous environments. The limited availability of open UXO data has, until now, impeded the progress of image-based UXO classification, which may offer a safe alternative at a reduced cost. In addition, the existing sporadic efforts focus mainly on small scale experiments using only a subset of common UXO categories. Our work aims to stimulate research interest in image-based UXO classification, with the curation of a novel dataset that consists of over 10000 annotated images from eight major UXO categories. Through extensive experimentation with supervised deep learning we uncover key insights into the challenging aspects of this task. Finally, we set the baseline on our novel benchmark by training state-of-the-art Convolutional Neural Networks and a Vision Transformer that are able to discriminate between highly overlapping UXO categories with 84.33% accuracy.
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