A review on the improvement of remote sensing image object detection methods based on the YOLO series algorithms
Abstract
Object detection in remote sensing images is critical for disaster monitoring, urban planning, and low-altitude defense. However, challenges such as extremely small target scales, complex background interference, and limited edge computing power hinder the performance of standard algorithms. This paper provides a systematic review of improvement strategies for YOLO-based object detection in remote sensing. We analyze key evolutionary directions, including perception-enhanced backbone networks, multi-scale feature fusion mechanisms, background-suppressing attention modules, and high-precision bounding box regression loss functions. Furthermore, this review categorizes cutting-edge applications in scenarios such as UAV monitoring, smart transportation, and multi-source sensor collaborative defense (e.g., electro-optical and radar integration). By evaluating the advantages and limitations of current methodologies, we conclude that the future of the field lies in the transition from single-modality visual perception to multi-dimensional collaborative detection systems. This review serves as a comprehensive reference for optimizing object detection algorithms tailored for complex remote sensing and low-altitude security environments.
