An airport apron ground service surveillance algorithm based on improved YOLO network

Yaxi Xu; Yi Liu; Ke Shi; Xin Wang; Yi Li; Jizong Chen; Yaxi Xu; Yi Liu; Ke Shi; Xin Wang; Yi Li; Jizong Chen

doi:10.3934/era.2024164

Electronic Research Archive

2024, Volume 32, Issue 5: 3569-3587. doi: 10.3934/era.2024164

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An airport apron ground service surveillance algorithm based on improved YOLO network

Yaxi Xu ¹,
Yi Liu ²,
Ke Shi ^{3
,
,},
Xin Wang ⁴,
Yi Li ⁴,
Jizong Chen ⁴

1.
School of Economics and Management, Civil Aviation Flight University of China, Guanghan 618307, China
2.
Department of Big Data and Artificial Intelligence, Civil Aviation Management Institute of China, Beijing 100102, China
3.
School of Civil Aviation Supervisor Training, Civil Aviation Flight University of China, Guanghan 618307, China
4.
School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China

Received: 04 February 2024 Revised: 12 May 2024 Accepted: 14 May 2024 Published: 03 June 2024

To assure operational safety in the airport apron area and track the process of ground service, it is necessary to analyze key targets and their activities in the airport apron surveillance videos. This research shows an activity identification algorithm for ground service objects in an airport apron area and proposes an improved YOLOv5 algorithm to increase the precision of small object detection by introducing an SPD-Conv (spath-to-depth-Conv) block in YOLOv5's backbone layer. The improved algorithm can efficiently extract the information features of small-sized objects, medium-sized objects, and moving objects in large scenes, and it achieves effective detection of activities of ground service in the apron area. The experimental results show that the detection average precision of all objects is more than 90%, and the whole class mean average precision (mAP) is 98.7%. At the same time, the original model was converted to TensorRT and OpenVINO format models, which increased the inference efficiency of the GPU and CPU by 55.3 and 137.1%, respectively.

Keywords:

Citation: Yaxi Xu, Yi Liu, Ke Shi, Xin Wang, Yi Li, Jizong Chen. An airport apron ground service surveillance algorithm based on improved YOLO network[J]. Electronic Research Archive, 2024, 32(5): 3569-3587. doi: 10.3934/era.2024164

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Abstract

A correction on

Numerical investigation and improvement of the aerodynamic performance of a modified elliptical-bladed Savonius-style wind turbine. By Sri Kurniati, Sudirman Syam and Arifin Sanusi. AIMS Energy, 2023, Volume 11, Issue 6: 1211–1230. Doi: 10.3934/energy.2023055

The authors would like to make the following corrections to the published paper [1].

On page 1213, we updated the contents of "one symbol statement: ρ" in section 2. The updated contents are as follows:

- ρ is the the density of air,

On page 1215, we updated the contents of "Eq 16" in section 2. The updated contents are as follows:

$\frac{{{\bf{∂}} {\bf{ \pmb{\mathsf{ ϕ}} }}}_{{\bf{ℓ}}}}{{\bf{∂}} \boldsymbol{t}}+{\bf{∇}} \left({{\varnothing }}_{{\bf{ℓ}}}\overrightarrow{\boldsymbol{V}}\right)-{\bf{∇}} \left({\bf{\Gamma }}_{{\bf{ℓ}}}\overrightarrow{\boldsymbol{V}}{{\varnothing }}_{{\bf{ℓ}}}\right) = {\boldsymbol{R}}_{{\bf{ℓ}}}$

(16)

On page 1216, we updated the contents of "Table 2" in section 2. The updated contents are as follows:

Table 2. The terms in the general transfer equation Eq 16.

$\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \frac{{\partial {\rm{ \mathsf{ ϕ} }}}_{\ell }}{\partial t}+\nabla \left({\varnothing }_{\ell }\overrightarrow{V}\right)-\nabla \left({\mathrm{\Gamma }}_{\ell }\overrightarrow{V}{\varnothing }_{\ell }\right)={R}_{\ell } \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(16)$
$l$	${\varnothing }_{\ell }$	${\mathrm{\Gamma }}_{\ell }$
1	U	$vt$	$-\frac{1}{\rho }\frac{\partial \rho }{\partial x}+\frac{\partial }{\partial x}\left(vt\frac{\partial u}{\partial x}\right)+\frac{\partial }{\partial y}\left(vt\frac{\partial v}{\partial x}\right)+\frac{\partial }{\partial z}\left(vt\frac{\partial w}{\partial x}\right)+gx$
2	V	$vt$	$-\frac{1}{\rho }\frac{\partial \rho }{\partial y}+\frac{\partial }{\partial x}\left(vt\frac{\partial u}{\partial y}\right)+\frac{\partial }{\partial y}\left(vt\frac{\partial v}{\partial y}\right)+\frac{\partial }{\partial z}\left(vt\frac{\partial w}{\partial y}\right)+gy$
3	W	$vt$	$-\frac{1}{\rho }\frac{\partial \rho }{\partial z}+\frac{\partial }{\partial x}\left(vt\frac{\partial u}{\partial z}\right)+\frac{\partial }{\partial y}\left(vt\frac{\partial v}{\partial z}\right)+\frac{\partial }{\partial z}\left(vt\frac{\partial w}{\partial z}\right)+gz$
4	1	0	0
5	K	$vt/$	$G-\varepsilon$
6	$\varepsilon$	$vt/$	${C}_{1}\frac{\varepsilon }{k}G-{c}_{2}\frac{\varepsilon }{k}\varepsilon$

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Conflict of interest

All authors declare no conflicts of interest in this paper.

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