Single shot detector for object detection using an ensemble of deep learning and statistical modelling for robot learning applications
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this study, a deep learning model was synthesized for object detection that can give better results than the state-of-the-art accuracy and reliability in production environments making it more accessible to production-ready applications. For this, a model architecture was used that is inspired by state-of-the-art Single Shot Detector architecture. Statistical analysis was performed on the results generated by the model to make final predictions in each frame. Experimental results outperform simple-SSD architecture. For 300 X 300 inputs, it achieves 84.7% mean Average Precision on the system with Nvidia GeForce GTX 1660 Ti 14GB, 16 GB ram, and Intel(R) Core (TM) i7-9750H CPU @ 2.60GHz at 60FPS, outperforming existing architectures of SSD, Faster Region - Convolutional Neural Network, You Only Look Once (YOLO), Resnet50, Resnet100 in use.