论文标题
用于小物体检测的扩展功能金字塔网络
Extended Feature Pyramid Network for Small Object Detection
论文作者
论文摘要
小物体检测仍然是一个未解决的挑战，因为很难提取只有几个像素的小物体的信息。尽管特征金字塔网络中的比例级相应检测减轻了此问题，但我们发现各种尺度的特征耦合仍然会损害小物体的性能。在本文中，我们提出了扩展特征金字塔网络（EFPN），其高分辨率金字塔水平专门用于小物体检测。具体而言，我们设计了一个新型模块，称为特征纹理传输（FTT），该模块用于超级溶解特征和同时提取可靠的区域细节。此外，我们设计了一个前景平衡的损失功能，以减轻前景和背景的区域不平衡。在我们的实验中，所提出的EFPN在计算和内存方面都是有效的，并且在小型交通符号数据集中产生最先进的结果，tsinghua-tencent 100k和一小类通用对象检测数据集MS Coco。
Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. In this paper, we propose extended feature pyramid network (EFPN) with an extra high-resolution pyramid level specialized for small object detection. Specifically, we design a novel module, named feature texture transfer (FTT), which is used to super-resolve features and extract credible regional details simultaneously. Moreover, we design a foreground-background-balanced loss function to alleviate area imbalance of foreground and background. In our experiments, the proposed EFPN is efficient on both computation and memory, and yields state-of-the-art results on small traffic-sign dataset Tsinghua-Tencent 100K and small category of general object detection dataset MS COCO.
