Object detection with class aware region proposal network and focused attention objective

Abstract

In this paper, we propose a novel deep CNN-based framework to improve object detection performance. First, we introduce the Class Aware Region Proposal Network (CARPN) to produce high quality region proposals by using a new strategy for anchor generation, and by training the network with both bounding boxes and category labels of the objects. Instead of learning a binary object/non-object classifier for generating region proposals, we assign the class label to each anchor, and train the region proposal network with a multi-class loss. Second, we introduce the Focused Attention (FA) objective to encourage the network to learn features mainly from objects of interest while suppressing those features from the background region. As a result, false positive proposals caused by strong background features can be reduced to a large extent. Comprehensive experimental evaluations reveal that the proposed CARPN & FA framework remarkably outperforms the baseline Faster R-CNN method up to 4.1% mAP with a shallower network and 2.8% mAP with a deeper network, and achieves a better mAP than most of the latest state-of-the-art methods.