4.6 Article

DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance

期刊

ELECTRONICS
卷 12, 期 15, 页码 -

出版社

MDPI
DOI: 10.3390/electronics12153296

关键词

object detection; UAV; security surveillance; feature pyramid network; attention mechanism

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Unmanned aerial vehicle (UAV) object detection technology is widely used for real-time collection and analysis of image data to determine the category and location of targets. However, detecting small-scale targets can be challenging and compromise security surveillance effectiveness. In this study, a novel dual-backbone network detection method (DB-YOLOv5) is proposed to enhance the extraction capability of small-scale target features and improve accuracy. Experimental results on the VisDrone-DET dataset demonstrate a 3% improvement over the benchmark model, highlighting the effectiveness of the proposed method. This approach enhances security surveillance in UAV object detection and provides a valuable tool for protecting critical infrastructure.
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.

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