An airport apron ground service surveillance algorithm based on improved YOLO network
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 apro...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-06-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024164?viewType=HTML |
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| author | Yaxi Xu Yi Liu Ke Shi Xin Wang Yi Li Jizong Chen |
| author_facet | Yaxi Xu Yi Liu Ke Shi Xin Wang Yi Li Jizong Chen |
| author_sort | Yaxi Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0c3e06d15df44e60b024fa9632e478db |
| institution | OA Journals |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-0c3e06d15df44e60b024fa9632e478db2025-08-20T01:54:26ZengAIMS PressElectronic Research Archive2688-15942024-06-013253569358710.3934/era.2024164An airport apron ground service surveillance algorithm based on improved YOLO networkYaxi Xu0Yi Liu1Ke Shi2Xin Wang3Yi Li4 Jizong Chen 51. School of Economics and Management, Civil Aviation Flight University of China, Guanghan 618307, China2. Department of Big Data and Artificial Intelligence, Civil Aviation Management Institute of China, Beijing 100102, China3. School of Civil Aviation Supervisor Training, Civil Aviation Flight University of China, Guanghan 618307, China4. School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China4. School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China4. School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaTo 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.https://www.aimspress.com/article/doi/10.3934/era.2024164?viewType=HTMLairport apronobject detectionservice activity recognitionintelligent video surveillance |
| spellingShingle | Yaxi Xu Yi Liu Ke Shi Xin Wang Yi Li Jizong Chen An airport apron ground service surveillance algorithm based on improved YOLO network Electronic Research Archive airport apron object detection service activity recognition intelligent video surveillance |
| title | An airport apron ground service surveillance algorithm based on improved YOLO network |
| title_full | An airport apron ground service surveillance algorithm based on improved YOLO network |
| title_fullStr | An airport apron ground service surveillance algorithm based on improved YOLO network |
| title_full_unstemmed | An airport apron ground service surveillance algorithm based on improved YOLO network |
| title_short | An airport apron ground service surveillance algorithm based on improved YOLO network |
| title_sort | airport apron ground service surveillance algorithm based on improved yolo network |
| topic | airport apron object detection service activity recognition intelligent video surveillance |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024164?viewType=HTML |
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