A Multiscale Feature Refinement Detector for Small Objects With Ambiguous Boundaries

There are multiple challenges in small object detection, including limited instances, insufficient features, diverse scales, uneven distribution, ambiguous boundaries, and complex backgrounds. These issues often lead to high false detection rates and hinder model generalization and convergence. This study proposes a multi-scale object detection algorithm that enhances the detection of subtle features by improving the detection head and incorporating a minimum point distance intersection-over-union loss. The enhanced detection head improves target representation, enabling more precise localization and classification of small objects. Meanwhile, the new loss function stabilizes bounding box regression by adaptively adjusting auxiliary bounding box scales. Evaluations on two benchmark datasets demonstrate that our method achieves a 2.6% increase in mAP50 and a 1.8% improvement in mAP50:95 on the Satellite Imagery Multivehicles dataset and a 1.9% increase in mAP50:95 on the DIOR dataset. Furthermore, the model reduces the number of parameters by 2.5% and the computational cost by 1.4%, demonstrating its potential for real-time detection applications.