上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 10: 1292 - 1304

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.303

交通运输工程

基于改进YOLOX-s的机场跑道冰雪状态感知

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  • 1.中国民航大学 电子信息与自动化学院, 天津 300300
    2.天津航空机电有限公司, 天津 300308
    3.中国民航局第二研究所 工程技术研究中心, 成都 610041
邢志伟(1970-),教授,从事机场运行控制,民航装备与系统的应用研究.

收稿日期: 2022-08-04

  修回日期: 2022-11-23

  录用日期: 2022-12-01

  网络出版日期: 2023-10-31

基金资助

国家重点研发计划(2018YFB1601200)

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Airport Pavement Snow and Ice State Perception Based on Improved YOLOX-s

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  • 1. School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
    2. AVIC Tianjin Aviation Machinery and Electricity Co., Ltd., Tianjin 300308, China
    3. Engineering Technology Research Centre, Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, China

Received date: 2022-08-04

  Revised date: 2022-11-23

  Accepted date: 2022-12-01

  Online published: 2023-10-31

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摘要

针对机场冰雪跑道安全性和适航性状态感知能力不足及跑道表面状况报告交互的新需求,提出一种面向多尺度特征融合的机场跑道冰雪状态感知模型.以YOLOX-s模型为基础,在主干特征提取网络中引入全局上下文模块,获取更丰富的浅层与深层特征;将颈部结构中路径聚合网络替换为双向特征金字塔,以提升特征融合能力;在加强特征提取网络尾部添加自适应空间特征融合结构,进一步增强特征融合效果;使用α-EIoU优化损失函数,提高模型收敛速度与精度.实验结果表明,改进后的YOLOX-s模型在跑道冰雪实验系统所得的冰雪污染物数据集上平均精度达到了91.53%,比原始的YOLOX-s模型提高了4.68%,能够为机场跑道除冰雪作业提供决策支持.

关键词： 跑道冰雪状态感知; YOLOX-s; 全局上下文模块; 双向特征金字塔网络; 自适应空间特征融合结构; α-EIoU损失函数

本文引用格式

邢志伟, 阚犇, 刘子硕, 李彪, 罗谦 . 基于改进YOLOX-s的机场跑道冰雪状态感知[J]. 上海交通大学学报, 2023 , 57(10) : 1292 -1304 . DOI: 10.16183/j.cnki.jsjtu.2022.303

Abstract

Aimed at the lack of awarness of safety and airworthiness state perception ability of airport ice runway and the new demand of interaction of runway surface condition report, a multi-scale feature fusion based ice and snow state perception model of airport runway is proposed. Based on the YOLOX-s model, first, the global context block (GC block) is introduced into the backbone feature extraction network to obtain more abundant shallow and deep features. Then, the PANet networks in neck are replaced with the bi-directional feature pyramid network (BiFPN) to improve the feature fusion ability. Afterwards, an adaptive spatial feature fusion (ASFF) structure is added to the tail of the enhanced feature extraction network to further enhance the feature fusion effect. Finally, α-EIoU is used to optimize the loss function to improve the convergence speed and accuracy of the model. The experimental results show that the improved YOLOX-s model has an average accuracy of 91.53% in the snow and ice pollutant data set obtained from the runway snow and ice experimental system, which is 4.68% higher than the original YOLOX-s model, and can provide decision-making support for airport runway snow removal operations.

Key words： pavement snow and ice state perception; YOLOX-s; global context block (GC block); bi-directional feature pyramid network (BiFPN); adaptive spatial feature fusion (ASFF); α-EIoU loss function

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