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.
翻译:未爆炸弹药对人类生命和环境构成巨大的危险。通过对战时图像的评估,专家可以推断出哑弹的发生。目前的人工分析过程既费钱又费时,因此,利用深层学习自动探测弹坑是改进未爆炸弹药处理过程的一个很有希望的方法。然而,这些方法需要大量人工标记的培训数据。这项工作利用卫星表面图像进行域域适应,在有限的培训数据的限制下,通过深层学习解决自动炸弹弹坑探测问题。这份文件有助于学术界和实践:(1) 以有限的培训数据为自动炸弹坑探测提供解决办法,(2) 展示利用合成图像进行域适应的实用性和相关挑战。