Realtime Object Detection Using TensorFlow an application of ML

Abstract

Efficient and accurate object detection has been an important topic in the advancement of computer vision systems. With the advent of deep learning techniques, the accuracy of object detection has increased drastically. The project aims to incorporate state-of-the-art techniques for object detection with the goal of achieving high accuracy with real-time performance. A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the deep learning-based approach, which leads to slow and non-optimal performance. In this project, we use a completely deep learning-based approach to solve the problem of object detection in an end-to-end fashion. The network is trained on the most challenging publicly available dataset (PASCAL VOC), on which an object detection challenge is conducted annually. The resulting system is fast and accurate, thus aiding those applications which require object detection.