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

doi:10.16183/j.cnki.jsjtu.2022.303

Abstract

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

CLC Number:

TP391.9

XING Zhiwei, KAN Ben, LIU Zishuo, LI Biao, LUO Qian. Airport Pavement Snow and Ice State Perception Based on Improved YOLOX-s[J]. Journal of Shanghai Jiao Tong University, 2023, 57(10): 1292-1304.

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状态	特点	晶体状态	灰度区间
雪	颗粒状,遇热变形	粒径0.05 mm以上互不相连的圆粒	48~80
雪浆	雪半融解产物	粒径0.5~1 mm的圆粒相互成网状连接	81~111
融雪	雪融解后产物	粒径1 mm以上互不相连的颗粒	235~255
冻冰	雨雪冻结而成	粒径0.05~2 mm的多结晶冰,含粒径0.01~0.1 mm气泡	112~144
湿冰	冻冰半融解产物	粒径0.1~0.4 mm的多结晶冰,含粒径0.1~0.5 mm气泡	145~207
水	冰雪完全融解产物含杂质	透明且流动不止,含有大量气泡	—

算法	φ_AP						ψ_mAP	?_mR	FPS/ (帧·s^-1)
算法	雪	雪浆	融雪	冻冰	湿冰	水	ψ_mAP	?_mR	FPS/ (帧·s^-1)
Faster R-CNN	0.789 8	0.842 1	0.814 0	0.833 4	0.794 2	0.922 5	0.832 7	0.702 5	18
SSD	0.764 3	0.820 1	0.801 0	0.805 6	0.779 5	0.911 5	0.813 7	0.683 4	16
YOLOv5-s	0.795 6	0.854 2	0.825 8	0.853 2	0.802 5	0.931 3	0.843 8	0.726 2	25
YOLOX-s	0.810 7	0.882 5	0.876 3	0.868 8	0.826 3	0.946 5	0.868 5	0.790 2	24
IYOLOX-s	0.871 1	0.915 4	0.923 9	0.915 8	0.883 8	0.981 9	0.915 3	0.834 5	23

组别	GC	BiFPN	ASFF	α-EIoU	ψ_mAP	?_mR
G1	×	×	×	×	0.868 5	0.790 2
G2	√	×	×	×	0.881 4	0.801 2
G3	√	√	×	×	0.892 1	0.812 3
G4	√	√	√	×	0.902 9	0.823 9
G5	√	√	√	√	0.915 3	0.834 5