Improving Small Object Detection with Area-Scaled and Dynamic Focal Loss
Siddharth Raina, Jeshwanth Challagundla, Sharada Acharya, M. P. Singh · 2025
Detecting small objects remains a persistent challenge for state-of-the-art object detection models, which excel at identifying larger objects but struggle with smaller ones due to limited pixel representation and susceptibility to noise and occlusion. This paper introduces two innovative loss-scaling strategies to address these challenges: Inverse Quadratic Scaling, which adjusts loss inversely proportional to the square root of object area, and Dynamic Focal Loss, which incorporates object size into the focal loss framework. These methods aim to improve training dynamics for small objects while preserving performance for larger objects. We evaluate our strategies on the CPPES dataset using a ResNet-50-based DETR model, characterized by significant object size variations. Experimental results demonstrate substantial improvements in small object detection, achieving F1 score increases of up to 274% in some categories compared to the baseline. Notably, Inverse Quadratic Scaling excels in enhancing underrepresented objects like goggles, while Dynamic Focal Loss offers balanced performance across sizes. These findings underscore the potential of tailored loss functions to mitigate small object detection challenges and provide a foundation for real-world applications such as medical imaging, autonomous vehicles, and surveillance systems.