  The effectiveness of Object Detection, one of the central problems in
computer vision tasks, highly depends on the definition of the loss function -
a measure of how accurately your ML model can predict the expected outcome.
Conventional object detection loss functions depend on aggregation of metrics
of bounding box regression such as the distance, overlap area and aspect ratio
of the predicted and ground truth boxes (i.e. GIoU, CIoU, ICIoU etc). However,
none of the methods proposed and used to date considers the direction of the
mismatch between the desired ground box and the predicted, "experimental" box.
This shortage results in slower and less effective convergence as the predicted
box can "wander around" during the training process and eventually end up
producing a worse model. In this paper a new loss function SIoU was suggested,
where penalty metrics were redefined considering the angle of the vector
between the desired regression. Applied to conventional Neural Networks and
datasets it is shown that SIoU improves both the speed of training and the
accuracy of the inference. The effectiveness of the proposed loss function was
revealed in a number of simulations and tests.
