关键词: 目标检测, 卷积神经网络（CNN）, 注意力机制, 特征降维

Abstract: In the Convolution Neural Network（CNN）, convolutional layers are translation-invariant, which weaken the localization performance of object detector. Actually, objects usually have distinct sub-region spatial characteristics and aspect ratio characteristics, but in prevalent two-stage object detection methods, these translation-variant feature components are rarely considered. In order to optimize the feature representations, the sub-region attention bank and aspect ratio attention bank are introduced into the two-stage object detection framework and generate the corresponding attention maps to refine the original ROI features.In addition, with the aid of the attention maps, the feature dimension can be greatly reduced.The experimental results show that object detectors equipped with attention module improve the accuracy and inference speed signi cantly.

Key words: object detection, Convolution Neural Network（CNN）, attention mechanism, dimension reduction