Human and Object Detection Deep Learning Model Using R-CNN

Abstract

Human and object detection is deep learning model. Which identifies and detects human(people) and object from image. For implementing the human and object detection there many popular algorithms like YOLO (You Only Look Once ), SSD (Single Shot Multi-Box Detector, CNN and R- CNN family. R-CNN family has R-CNN (Region Based Convolution Neural Network), FAST R-CNN, FASTER R- CNN. This time the R-CNN is very popular in market for more accuracy and efficient machine and deep learning object detection model. In this paper we have explain about algorithm and implementation of the human and object detection deep learning and machine learning model using Faster R-CNN and CV2. Faster R-CNN model take less time and create efficient model with more accuracy as comparison to Fast R-CNN and R-CNN [3]. For identifying object or human from image we have fast working approach that is Faster R-CNN with accuracy 97.85%, precision 89.80% and recall 91.30%.