Object Detection of Furniture and Home Goods Using Advanced Computer Vision

Abstract

Object Detection Technology has been a subject to much research and development due to increasing use of images and videos as data sources and their huge number of applications. Traditional models for object detection had limitations in training and did not use transfer learning for their benefit. With the evolution of deep learning and Neural networks, newer and powerful tools have made way to achieve Object Detection in real-time, with the added advantage of transfer learning and detection of multiple instances of different classes of interest in the given image context. The proposed system is an Object Detection model based on the Single Shot Detector (SSD) algorithm trained with MobileNetV2 feature extraction that can be utilized and integrated in e-commerce, hospitality industry, security and surveillance, real estate, self-driving cars and floor inventory management.