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.