跑道
计算机科学
目标检测
预处理器
对象(语法)
数据挖掘
领域(数学)
数据预处理
人工智能
公制(单位)
构造(python库)
比例(比率)
注释
计算机视觉
模式识别(心理学)
地理
工程类
地图学
数学
程序设计语言
纯数学
运营管理
作者
Heng Zhang,Wei Fu,Jiabo Shao,Dong Li,Xiaoming Wang
标识
DOI:10.1109/itoec57671.2023.10291472
摘要
To address the current issues in the field of airport runway foreign object detection, such as small data volume, low data quality, and limited data variety, a road foreign object dataset is proposed in this paper. The dataset is created through large-scale data collection and annotation, aiming to simulate the foreign object detection scenarios on airport runways and construct a diverse and detailed road foreign object dataset. This dataset consists of 12 categories of foreign objects, including metal devices, mechanical tools, and miscellaneous items. Furthermore, the dataset proposed in this paper was trained and tested using commonly used object detection algorithms. The experimental results demonstrated the significance of preprocessing the original dataset, and the Mean Average Precision(mAP) metric of the YOLO-V5-n algorithm cited in this paper outperformed other algorithms. The road debris dataset proposed in this paper provides a valuable database for the airport runway debris detection field.
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