Real-Time Object Detection With Reduced Region Proposal Network via Multi-Feature Concatenation

In recent years, object detection became more and more important following the successful results from studies in deep learning. Two types of neural network architectures are used for object detection: one-stage and two-stage. In this paper, we analyze a widely used two-stage architecture called Faster R-CNN to improve the inference time and achieve real-time object detection without compromising on accuracy. To increase the computation efficiency, pruning is first adopted to reduce the weights in convolutional and fully connected (FC) layers. However, this reduces the accuracy of detection. To address this loss in accuracy, we propose a reduced region proposal network (RRPN) with dilated convolution and concatenation of multi-scale features. In the assisted multi-feature concatenation, we propose the intra-layer concatenation and proposal refinement to efficiently integrate the feature maps from different convolutional layers; this is then provided as an input to the RRPN. Using the proposed method, the network can find object bounding boxes more accurately, thus compensating for the loss arising from compression. Finally, we test the proposed architecture using ZF-Net and VGG16 as a backbone network on the image sets in PASCAL VOC 2007 or VOC 2012. The results show that we can compress the parameters of the ZF-Net-based network by 81.2% and save 66% of computation. The parameters of VGG16-based network are compressed by 73% and save 77% of computation. Consequently, the inference speed is improved from 27 to 40 frames/s for ZF-Net and 9 to 27 frames/s for VGG16. Despite significant compression rates, the accuracy of ZF-Net is increased from 2.2% to 60.2% mean average precision (mAP) and that of VGG16 is increased from 2.6% to 69.1% mAP.