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.

有效的边界框回归对于以可接受的精度运行任何实时对象检测算法至关重要。当前可用的损失函数存在计算量大等问题，有时它们会遇到非重叠边界框的平稳问题，因为结果边界框被发现与真实情况相去甚远。在目前的调查中，我们提出了一个损失函数，其中引入了一个新的幂律项，用于归一化距离，其收敛速度与联合完全交集 (CIoU) 一样快，但结果证明在计算上比联合交集 (IoU) 和广义 IoU (GIoU)。提议的功能比 CIoU 更简单。根据相应的计算时间和模拟约数百万个案例的误差总和，对合并的幂项进行了优化，详细信息已在论文中详细说明。所提出的绝对 IoU (AIoU) 损失函数已使用最先进的对象检测算法成功实现和测试，例如 You Only Look Once (YOLO) 和 Single Shot Multibox Detector (SSD)，并发现可以实现使用著名的度量平均精度（AP）可以显着提高性能，表明我们的方法有效。