论文标题
基于水平高频信号的高性能对象建议
A High-Performance Object Proposals based on Horizontal High Frequency Signal
论文作者
论文摘要
近年来，将对象建议用作目标检测的预处理步骤，以提高计算效率已成为一种有效的方法。良好的对象建议方法应具有较高的对象检测召回率和低计算成本，以及良好的本地化质量和可重复性。但是，当前高级算法很难在上述性能中取得良好的平衡。对于这个问题，我们提出了一种独立于班级的对象建议算法BIHL。它结合了窗口评分的优势和超级像素合并，这不仅提高了本地化质量，还可以加快计算效率。 VOC2007数据集的实验结果表明，当IOU为0.5和10,000预算提案时，我们的方法可以达到最高的检测召回率，平均最佳重叠为79.5％，并且计算效率的速度比当前最快的方法快三倍。此外，我们的方法是在各种干扰方面具有良好重复性的方法中平均重复性最高的方法。
In recent years, the use of object proposal as a preprocessing step for target detection to improve computational efficiency has become an effective method. Good object proposal methods should have high object detection recall rate and low computational cost, as well as good localization quality and repeatability. However, it is difficult for current advanced algorithms to achieve a good balance in the above performance. For this problem, we propose a class-independent object proposal algorithm BIHL. It combines the advantages of window scoring and superpixel merging, which not only improves the localization quality but also speeds up the computational efficiency. The experimental results on the VOC2007 data set show that when the IOU is 0.5 and 10,000 budget proposals, our method can achieve the highest detection recall and an mean average best overlap of 79.5%, and the computational efficiency is nearly three times faster than the current fastest method. Moreover, our method is the method with the highest average repeatability among the methods that achieve good repeatability to various disturbances.
