FOD-A: A Dataset for Foreign Object Debris in Airports
FOD-A: A Dataset for Foreign Object Debris in Airports
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发表时间：
2021-10
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通讯作者：
Travis J. E. Munyer;Pei-Chi Huang;Chenyu Huang;Xin Zhong
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作者：
Travis J. E. Munyer;Pei-Chi Huang;Chenyu Huang;Xin Zhong
异物碎片（FOD）检测在机器学习和计算机视觉领域受到越来越多的关注。然而，尚未初始化一个健壮且公开可用的FOD图像数据集。为此，本文引入了一个FOD图像数据集，命名为机场FOD （FOD- a）。FOD-A对象类别是根据联邦航空管理局（FAA）先前文件和相关研究的指导进行选择的。除了用于目标检测的边界框的主要注释外，FOD-A还提供了标记的环境条件。因此，每个注释实例进一步分为三个光照级别类别（亮、暗和暗）和两个天气类别（干和湿）。目前，FOD-A已经发布了31个对象类别和3万多个标注实例。本文介绍了创建方法，讨论了公开可用的数据集扩展过程，并展示了FOD-A与广泛使用的机器学习模型用于目标检测的实用性。
Foreign Object Debris (FOD) detection has attracted increased attention in the area of machine learning and computer vision. However, a robust and publicly available image dataset for FOD has not been initialized. To this end, this paper introduces an image dataset of FOD, named FOD in Airports (FOD-A). FOD-A object categories have been selected based on guidance from prior documentation and related research by the Federal Aviation Administration (FAA). In addition to the primary annotations of bounding boxes for object detection, FOD-A provides labeled environmental conditions. As such, each annotation instance is further categorized into three light level categories (bright, dim, and dark) and two weather categories (dry and wet). Currently, FOD-A has released 31 object categories and over 30,000 annotation instances. This paper presents the creation methodology, discusses the publicly available dataset extension process, and demonstrates the practicality of FOD-A with widely used machine learning models for object detection.