Object Detection and Recognition in AI Systems - SIT740

This report investigates the application of object detection techniques in Artificial Intelligence, particularly focusing on real-time object recognition within robotics. The study highlights the challenges of programming robots to recognize real-world objects such as faces, bicycles, and buildings. The report discusses the need for efficient object detection algorithms and the importance of quick response times for commercial applications. It references relevant research, including studies on deep neural networks and multipath networks for object detection. The goal is to provide robots with the ability to perceive and interact with their environment effectively. The report emphasizes the importance of object detection in AI and robotics, with the need for further advancements in this field.

Running head: RDIT - IDEATION
SIT740 Research and Development in Information Technology
Pass Task 8.1: Ideation
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1RDIT - IDEATION
Recognition of real time world objects with Object detection
The rise in the study of Artificial Intelligence has offered advancement in robotics however there
still exists a complex issue of programming for real world object recognition. The detection and
recognizing of real world objects requires application of suitable logic so the robots need to
gather information about the objects in an environment. Algorithms are needed for providing the
robots with such sort of skills as the objects are considered as the information about a particular
environment. Many research projects are being undertaken in context to the problem of
embedding the object detection technique in robots but in a controlled environment. Hence, this
study also focuses on the process of teaching the robots to recognize world objects such as faces,
bicycles, and buildings using object detection. The response time for detection of an object by
robots have to be sufficiently efficient so that those can be used for commercial purposes.

2RDIT - IDEATION
Bibliography
Erhan, D., Szegedy, C., Toshev, A. and Anguelov, D., 2014. Scalable object detection using deep
neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (pp. 2147-2154).
Zagoruyko, S., Lerer, A., Lin, T.Y., Pinheiro, P.O., Gross, S., Chintala, S. and Dollár, P., 2016.
A multipath network for object detection. arXiv preprint arXiv:1604.02135.