YOLORemote: Advancing Remote Sensing Object Detection by Integrating YOLOv8 With the CE-WA-CS Feature Fusion Approach

The rapid development iteration of YOLO models has spurred extensive research into specialized adaptations tailored for remote sensing object detection (RSOD). Typically, these adaptations involve modifying specific YOLO versions to excel in dataset-specific benchmarks. Such efforts primarily focus on enhancing model performance on specific datasets, a trend that, while beneficial for achieving high leaderboard standings, frequently overlooks the critical aspects of generalizability, scalability, and transferability. Consequently, there is a significant gap in the research concerning the development of universally applicable modules that are easy to adapt across various baseline YOLO models—an essential factor for practical deployment and widespread adoption in real-world applications. This study addresses these shortcomings by introducing a novel feature fusion method, context enhancement (CE), weight adjustment-based spatial attention (WA), and channel shuffling (CS)—collectively termed CE-WA-CS, specifically designed for RSOD. This research rigorously tests the proposed feature fusion method across various YOLO models, substantiating its versatility and effectiveness in enhancing RSOD. Notably, in comparative performance evaluations involving several YOLO architectures, integrating our feature fusion method with YOLOv8 emerged as the most balanced in terms of accuracy, computational complexity, and inference speed. This optimal combination has been designated as YOLORemote. YOLORemote not only exemplifies a significant improvement in performance metrics but also demonstrates a practical balance that is crucial for real-time applications.