Real-time Object Detection Using Deep Learning
K. Vaishnavi
Department of IT, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
G. Pranay Reddy
Department of IT, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
T. Balaram Reddy
Department of IT, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
N. Ch. Srimannarayana Iyengar
Department of IT, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
Subhani Shaik *
Department of IT, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
As technology improved, object detection, which is connected to video and image analysis, caught researchers' interest. Earlier object recognition techniques are based on hand-crafted features and imprecise architectures and trainable algorithms. One of the main issues with many object detection systems is that they rely on other computer vision methods to support their deep learning-based methodology, which leads to slow and subpar performance. In this article, we present an end-to-end solution to the object detection problem using a deep learning based method. The single shot detector (SSD) technique is the quickest method for object detection from an image using a single layer of a convolution network. Our research's primary goal is to enhance accuracy of SSD method.
Keywords: Object detection, SSD method, deep learning