AI based Object detection Robot through live streaming

Authors

  • Praveen Kumar G Department. of ECE, National Institute of Engineering, Mysuru, India
  • Keerthankumar K V
  • Prateek Shiggavi
  • Vinayak kamaraddi
  • Kavitha S S

DOI:

https://doi.org/10.5281/zenodo.5172486

Abstract

The need for Artificial Intelligence is increasing due to the increase in the complexity of modern world. The amount of data that is generated, by both humans and machines, far outperforms humans’ ability to captivate, infer and make complex decisions based on that data. Artificial intelligence is the foundation to all computer learning and elucidates all complex decisions. AI based Object detection is one of the solutions that can be implemented in face detection, pedestrian detection, vehicle detection, military and etc. Object detection is one of the most basic and central tasks in computer vision. Its task is to find all the concerned objects in the image, and determine the category and location of the objects. In recent times, with the development of convolutional neural network, significant advances have been made in object detection.

AI combined with Robotics has made us reach far more than normal in solving these problems. Robotics include design, assembly, operation, and use of robots. The objective of robotics is to design machines that can benefit humans. In future mankind would be mainly dependent on robots. In this project we have used the hybrid technology of AI and Robotics. Here AI based Object detection is done using a Robot, as the robot is mounted with the hardware module for object detection, which include microcontroller, camera for streaming and circuit components, and can be programmed for the purpose, i.e. robot manoeuvring and object detection enactments.

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Published

2021-08-09

How to Cite

Praveen Kumar G, Keerthankumar K V, Prateek Shiggavi, Vinayak kamaraddi, & Kavitha S S. (2021). AI based Object detection Robot through live streaming. International Journal of Advanced Scientific Innovation, 2(2). https://doi.org/10.5281/zenodo.5172486