A Real-Time Unmanned Aerial Vehicle (UAV) Aerial Image Object Detection Model
Li Tan, Zikang Liu, Liu He, Dongfang Li, Chen Zhang
Abstract
To address the challenges present in the increasingly popular field of drone aerial photography for target detection tasks, this paper aims to refer to the design paradigm of RT-DETR, and make adaptive improvements targeting issues in drone aerial images such as the high proportion of small targets, complex backgrounds, and the performance impact caused by threshold filtering and post-processing operations in traditional CNN-based target detection models. Firstly, by redesigning the feature fusion part of the model, a Global Feature Aggregation module is proposed on the basis of the traditional PAN, to enhance the feature fusion capability and reduce feature loss. Secondly, by incorporating the Bi-Level Routing Attention from BiFormer to construct the encoder structure of DETR, the model employs a dynamic sparse attention mechanism to focus on small targets in the images, thus strengthening the detection capability for minute targets. We propose an end-to-end drone aerial image target detection model based on the Transformer architecture, RT-DETR-UAVs. Through extensive experiments, the effectiveness and precision of the model are demonstrated. Furthermore, this model is applied to the task of aerial solar panel detection, discussing the diversity and potential of drone technology in the solar energy field, especially in terms of improving efficiency, reducing costs, and supporting sustainable development.