DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance
Unmanned aerial vehicle (UAV) object detection technology is widely used in security surveillance applications, allowing for real-time collection and analysis of image data from camera equipment carried by a UAV to determine the category and location of all targets in the collected images. However,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/15/3296 |
| _version_ | 1797586881850900480 |
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| author | Yuzhao Liu Wan Li Li Tan Xiaokai Huang Hongtao Zhang Xujie Jiang |
| author_facet | Yuzhao Liu Wan Li Li Tan Xiaokai Huang Hongtao Zhang Xujie Jiang |
| author_sort | Yuzhao Liu |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) object detection technology is widely used in security surveillance applications, allowing for real-time collection and analysis of image data from camera equipment carried by a UAV to determine the category and location of all targets in the collected images. However, small-scale targets can be difficult to detect and can compromise the effectiveness of security surveillance. In this work, we propose a novel dual-backbone network detection method (DB-YOLOv5) that uses multiple composite backbone networks to enhance the extraction capability of small-scale targets’ features and improve the accuracy of the object detection model. We introduce a bi-directional feature pyramid network for multi-scale feature learning and a spatial pyramidal attention mechanism to enhance the network’s ability to detect small-scale targets during the object detection process. Experimental results on the challenging UAV aerial photography dataset VisDrone-DET demonstrate the effectiveness of our proposed method, with a 3% improvement over the benchmark model. Our approach can enhance security surveillance in UAV object detection, providing a valuable tool for monitoring and protecting critical infrastructure. |
| first_indexed | 2024-03-11T00:28:27Z |
| format | Article |
| id | doaj.art-73d67fc4a99b45d1ba3048365d8fea7f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T00:28:27Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-73d67fc4a99b45d1ba3048365d8fea7f2023-11-18T22:49:06ZengMDPI AGElectronics2079-92922023-07-011215329610.3390/electronics12153296DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security SurveillanceYuzhao Liu0Wan Li1Li Tan2Xiaokai Huang3Hongtao Zhang4Xujie Jiang5School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, ChinaUnmanned aerial vehicle (UAV) object detection technology is widely used in security surveillance applications, allowing for real-time collection and analysis of image data from camera equipment carried by a UAV to determine the category and location of all targets in the collected images. However, small-scale targets can be difficult to detect and can compromise the effectiveness of security surveillance. In this work, we propose a novel dual-backbone network detection method (DB-YOLOv5) that uses multiple composite backbone networks to enhance the extraction capability of small-scale targets’ features and improve the accuracy of the object detection model. We introduce a bi-directional feature pyramid network for multi-scale feature learning and a spatial pyramidal attention mechanism to enhance the network’s ability to detect small-scale targets during the object detection process. Experimental results on the challenging UAV aerial photography dataset VisDrone-DET demonstrate the effectiveness of our proposed method, with a 3% improvement over the benchmark model. Our approach can enhance security surveillance in UAV object detection, providing a valuable tool for monitoring and protecting critical infrastructure.https://www.mdpi.com/2079-9292/12/15/3296object detectionUAVsecurity surveillancefeature pyramid networkattention mechanism |
| spellingShingle | Yuzhao Liu Wan Li Li Tan Xiaokai Huang Hongtao Zhang Xujie Jiang DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance Electronics object detection UAV security surveillance feature pyramid network attention mechanism |
| title | DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance |
| title_full | DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance |
| title_fullStr | DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance |
| title_full_unstemmed | DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance |
| title_short | DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance |
| title_sort | db yolov5 a uav object detection model based on dual backbone network for security surveillance |
| topic | object detection UAV security surveillance feature pyramid network attention mechanism |
| url | https://www.mdpi.com/2079-9292/12/15/3296 |
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