A global object-oriented dynamic network for low-altitude remote sensing object detection

With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm's scalability and practical applicability. Visualization methods further highlight the algorithm's performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.