Research on Small Object Detection in Remote Sensing Images Based on Deep Learning

Small object detection in remote sensing images has significant applications in fields such as military operations, environmental monitoring, and disaster warning. However, traditional detection methods face challenges such as small target size, complex background, and low signal-to-noise ratio, leading to suboptimal detection performance. In recent years, deep learning technology, with its powerful feature extraction capabilities, has gradually become a key tool for improving the performance of small object detection. This paper designs an efficient model for small object detection in remote sensing images based on deep learning algorithms, focusing on dataset construction, network architecture design, and training optimization strategies. Through experimental validation, the proposed algorithm outperforms traditional methods in both accuracy and efficiency. Furthermore, this paper analyzes the potential engineering applications of the algorithm and discusses the challenges and future research directions.