Designing effective power law-based loss function for faster and better bounding box regression

Effective bounding box regression is essential for running any real-time object detection algorithm with acceptable accuracy. The currently available loss functions have issues like high computations, and sometimes they suffer from a subtle problem of plateau for non-overlapping bounding boxes, as the resultant bounding boxes are found to be far from the ground truth. In the present investigation, we have proposed a loss function with a new power-law term introduced in it for the normalized distance, which converges as fast as the Complete Intersection over Union (CIoU), but turns out to be computationally much faster than the Intersection over Union (IoU) and Generalised IoU (GIoU). The proposed function is simpler than CIoU. The incorporated power term has been optimized based on the corresponding computational time and on the sum of errors simulated for about multi-million cases, the details of which have been elaborated in the paper. The proposed Absolute IoU (AIoU) loss function has been successfully implemented and tested using the state-of-the-art object detection algorithms, such as You Only Look Once (YOLO) and Single Shot Multibox Detector (SSD) and is found to achieve significant performance improvement, using well-known metric Average Precision (AP), indicating the effectiveness of our approach.