With the rapid development of detectors, Bounding Box Regression (BBR) loss function has constantly updated and optimized. However, the existing IoU-based BBR still focus on accelerating convergence by adding new loss terms, ignoring the limitations of IoU loss term itself. Although theoretically IoU loss can effectively describe the state of bounding box regression,in practical applications, it cannot adjust itself according to different detectors and detection tasks, and does not have strong generalization. Based on the above, we first analyzed the BBR model and concluded that distinguishing different regression samples and using different scales of auxiliary bounding boxes to calculate losses can effectively accelerate the bounding box regression process. For high IoU samples, using smaller auxiliary bounding boxes to calculate losses can accelerate convergence, while larger auxiliary bounding boxes are suitable for low IoU samples. Then, we propose Inner-IoU loss, which calculates IoU loss through auxiliary bounding boxes. For different datasets and detectors, we introduce a scaling factor ratio to control the scale size of the auxiliary bounding boxes for calculating losses. Finally, integrate Inner-IoU into the existing IoU-based loss functions for simulation and comparative experiments. The experiment result demonstrate a further enhancement in detection performance with the utilization of the method proposed in this paper, verifying the effectiveness and generalization ability of Inner-IoU loss.

翻译：随着检测器的快速发展，边界框回归损失函数不断更新与优化。然而，现有基于IoU的边界框回归仍侧重于通过添加新损失项来加速收敛，忽略了IoU损失项自身的局限性。尽管理论上IoU损失能够有效描述边界框回归状态，但在实际应用中，其无法根据不同的检测器与检测任务进行自适应调整，泛化能力较弱。基于上述问题，我们首先分析了边界框回归模型，得出区分不同回归样本并采用不同尺度的辅助边界框计算损失可有效加速边界框回归过程的结论。对于高IoU样本，使用较小辅助边界框计算损失可加速收敛，而较大辅助边界框则适用于低IoU样本。随后，我们提出Inner-IoU损失，通过辅助边界框计算IoU损失。针对不同数据集与检测器，引入缩放因子ratio来控制计算损失时辅助边界框的尺度大小。最后，将Inner-IoU集成至现有基于IoU的损失函数中开展仿真与对比实验。实验结果表明，采用本文所提方法进一步提升了检测性能，验证了Inner-IoU损失的有效性与泛化能力。