论文标题
在空中图像中检测炸弹陨石坑的深层域适应
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images
论文作者
论文摘要
发生毁灭性事件后，数十年来仍然可以看到空袭的后果。未爆炸的军械（UXO）是对人类生活和环境的巨大危险。通过评估战时图像，专家可以推断出DUD的发生。当前的手动分析过程是昂贵且耗时的，因此使用深度学习通过自动检测炸弹坑是改善UXO处置过程的一种有希望的方法。但是，这些方法需要大量手动标记的培训数据。这项工作利用月球表面图像来利用域的适应性，以解决自动化炸弹火山口检测的问题，并在有限的训练数据的限制下深入学习。本文通过提供有限的训练数据和（2）的自动化炸弹火山口检测的解决方案方法来促进学术和实践（1），并通过证明将合成图像用于域适应的可用性和相关挑战。
The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation.
