  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. Code is available at
https://github.com/malagoutou/Inner-IoU.
