An airport apron ground service surveillance algorithm based on improved YOLO network-Reference-Cited by-同舟云学术

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

Published:2024 Issue:5 Volume:32 Page:3569-3587
ISSN:2688-1594
Container-title:Electronic Research Archive
language:
Short-container-title:era

Author:

Xu Yaxi¹,Liu Yi²,Shi Ke³,Wang Xin⁴,Li Yi⁴,Chen Jizong⁴

Affiliation:

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

Abstract

<abstract> <p>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.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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