Locate then Calibrate: A Synergistic Framework for Small Object Detection from Aerial Imagery to Ground-Level Views

Detection of small objects in aerial images captured by Unmanned Aerial Vehicles (UAVs) is a critical task in remote sensing. It is vital for applications like urban monitoring and disaster assessment. This task, however, is challenged by unique viewpoints, diminutive target sizes, and dense scenes. To surmount these challenges, this paper introduces the Locate then Calibrate (LTC) framework. It is a deep learning architecture designed to enhance visual perception systems, specifically for the accurate and robust detection of small objects. Our model builds upon the YOLOv8 architecture and incorporates three synergistic innovations. (1) An Efficient Multi-Scale Attention (EMA) mechanism is employed to `Locate’ salient targets by capturing critical cross-dimensional dependencies. (2) We propose a novel Adaptive Multi-Scale (AMS) convolution module to `Calibrate’ features, using dynamically learned weights to optimally fuse multi-scale information. (3) An additional high-resolution P2 detection head preserves the fine-grained details essential for localizing diminutive targets. Extensive experimental evaluations demonstrate that the proposed model substantially outperforms the YOLOv8n baseline. Notably, it achieves significant performance gains on the challenging VisDrone aerial dataset. On this dataset, the model achieves a remarkable 11.7% relative increase in mean Average Precision (mAP50). The framework also shows strong generalization. Considerable improvements are recorded on ground-level autonomous driving benchmarks such as KITTI and TT100K_mini. This validated effectiveness proves that LTC is a robust solution for high-accuracy detection: it achieves significant accuracy gains at the cost of a deliberate increase in computational GFLOPs, while maintaining a lightweight parameter count. This design choice positions LTC as a solution for edge applications where accuracy is prioritized over minimal computational cost.