Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage. Faster R-CNN is such an approach for object detection which combines both stages into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by its weak performance on small object instances, we examine in detail both the proposal and the classification stage with respect to a wide range of object sizes. We investigate the influence of feature map resolution on the performance of those stages. Based on theoretical considerations, we introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the FlickrLogos dataset improving the RPN performance from 0.52 to 0.71 (MABO) and the detection performance from 0.52 to 0.67 (mAP).

本研究旨在探讨以 Faster R-CNN 为代表的物体检测方法在公司商标检测任务中的应用。针对小物体实例表现不佳的问题，我们对该方法中的候选区域生成和分类两个阶段进行了研究，提出了一种改进的候选框生成机制，并利用更高分辨率的特征图来提高小物体检测性能，实验证明其在 FlickrLogos 数据集上 RPN 性能从 0.52 提高到 0.71（MABO），检测性能从 0.52 提高到 0.67（mAP）。

改进小物体建议以实现公司商标检测