Accurate object classification and detection by faster-RCNN

Recent advances in object detection algorithms include fast and faster RCNN which made the detection times comparatively low with high accuracy. In this work we verify the integrity of a proposed algorithm which uses RPN (Region proposal networks) and Fast RCNN (Region based Convolutional Neural Networks) for the detection. The RPN provides region proposals from that we give the ROI (Regions of Interest) as input to the RCNN network, it can be further merged into a single network by sharing their convolutional features to detect a specific object in a given image. As we use a unified network there is no need to get the ROI from an external network which makes this process cost free. We trained VGGnet with two different data sets PASCAL VOC 2012 and MS COCO on a low cost GPU and verified the accuracies while comparing the outputs with increasing number of region proposals. As we increased the number of proposals we observed a significant increase in the mAP (Mean Average Precision) value till 2000 proposals from where it reached saturation. Our results are compared with the state of the art algorithm with an increase of 1.2% in terms of mAP for 1800 proposals.