A study on UAV target detection and 3D positioning methods based on the improved deformable DETR model and multi-view geometry - Xuebin Liu; Hanshan Li (Advances in Mechanical Engineering)
This paper addresses critical challenges in Unmanned Aerial Vehicle (UAV) target detection and 3D positioning, specifically inaccuracies in localization and lack of robustness in complex environments. The objective of this research is to improve UAV detection and positioning accuracy by proposing an enhanced Deformable DETR (Detection Transformer) model integrated with multi-view geometry theory. To achieve this, the study first preprocesses UAV-collected data, then optimizes the convolutional layers of the original DETR model to better handle object occlusion and scale variations. Furthermore, the research incorporates multi-view geometric modeling and multimodal fusion strategies to enhance detection accuracy during the target recognition process. Experimental results demonstrate that the proposed approach achieves over 70% detection accuracy, significantly outperforming traditional methods. The findings underscore the effectiveness of combining the improved Deformable DETR model with multi-view geometry for high-precision detection and 3D localization in complex environments. This research has significant implications for UAV-based applications, such as autonomous navigation, surveillance, and search-and-rescue missions, where precise target detection and 3D positioning are critical for successful operation.