《Distance-IoU Loss：Faster and Better Learning for Bounding Box Regression》论文笔记
代码地址：DIoU 1. 概述 导读：这篇文章主要的贡献在边界框损失函数的优化上面，传统上使用 L n L_n Ln范数的损失，如smooth-L1或是其对应的改进balanced-L1，但是这些损失函数并不直接与检测框的质量相关。将检测框的质量作为回归的度量就引入IoU损失函数，为了弥补IoU损失函数在不相交的时候不可导的问题引入GIoU，但是GIoU也是存在收敛速度慢且回归不准确（GIoU在
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