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Region-based Object Detection and Classification using Faster R-CNN
Abstract
With the advent of Deep Learning the machine learning systems are able to recognize and classify objects of interest in an image. Various advancement has been done in the field of object recognition and classification. Our research work focusses on improving the R-CNN, Fast R-CNN, YOLO architecture. The work focused on using Region Proposals Network (RPN) to extract region of interest in an image. RPN outputs an image based on the objectness score. The output objects are subjected to Roll Polling for classification. Our research work focusses on training Faster R-CNN using custom based data set of images. Our trained network efficiently detects objects from an image consisting of multiple objects. Our network requires minimum GPU capability of 3. 0 or higher.
Keywords: Convolution neural network, deep learning, faster R-CNN, region proposal network
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Object Detection using fasterR-CNN,2017[Online]
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