This paper addresses the limitations of existing bounding box regression (BBR) loss functions in object detection, which often suffer from slow convergence and inaccurate regression results. The authors propose an Efficient Intersection over Union (EIOU) loss, which explicitly measures the discrepancies in three key geometric factors: overlap area, central point, and side length. They also introduce the Effective Example Mining (EEM) problem and design a regression version of focal loss to enhance the contributions of high-quality anchor boxes with large IOUs. The combined loss function, named Focal-EIOU, is evaluated on synthetic and real datasets, demonstrating superior performance in both convergence speed and localization accuracy. Extensive experiments show that the Focal-EIOU loss outperforms other BBR losses, achieving significant improvements when incorporated into state-of-the-art object detection models.This paper addresses the limitations of existing bounding box regression (BBR) loss functions in object detection, which often suffer from slow convergence and inaccurate regression results. The authors propose an Efficient Intersection over Union (EIOU) loss, which explicitly measures the discrepancies in three key geometric factors: overlap area, central point, and side length. They also introduce the Effective Example Mining (EEM) problem and design a regression version of focal loss to enhance the contributions of high-quality anchor boxes with large IOUs. The combined loss function, named Focal-EIOU, is evaluated on synthetic and real datasets, demonstrating superior performance in both convergence speed and localization accuracy. Extensive experiments show that the Focal-EIOU loss outperforms other BBR losses, achieving significant improvements when incorporated into state-of-the-art object detection models.