A Decisive Object Detection using Deep Learning Techniques
Padma P1, Srinivasan Selvaraj2, Ghayathri J3, Suryakumar P4

1Padma P, Department of Information Technology, Sri Sai Ram Engineering College, Chennai (Tamil Nadu), India.

2Srinivasan Selvaraj, Department of CSE, RMD Engineering College, Chennai (Tamil Nadu), India.

3Ghayathri J, Department of Information Technology, Sri Sai Ram Engineering College, Chennai (Tamil Nadu), India.

4Suryakumar P, Department of Information Technology, Sri Sai Ram Engineering College, Chennai (Tamil Nadu), India.

Manuscript received on 27 November 2019 | Revised Manuscript received on 07 December 2019 | Manuscript Published on 14 December 2019 | PP: 414-417 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10821191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1082.1191S19

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Object detection is one of the essential features of computer vision and image processing techniques. In today’s world, the computer can replicate or outperform the operation that a human can do. One such thing is object detection, and In the case of it, the machines must be trained in such a way that it can recognize the object equivalent to the human does with maximum accuracy. Several object detection techniques are used to train the machine to detect the objects. Some of the most common object detection techniques are R-CNN, Fast R-CNN, Faster R-CNN) Single Shot MultiBox Detector (SSD), and You Only Look Once(YOLO),. Each of these techniques has a different way of approach and accuracy of detecting the objects in real-time. These techniques are differentiated based on their performances, i.e., speed and accuracy. Some techniques may be very accurate in detecting the objects but may lack in the time taken for detecting the objects, whereas, on the other hand, some techniques may be very fast in figuring out the objects but not with greater accuracy. We have trained an object detection model based on the YOLO technique which gave the best performance out of all other existing techniques, though the accuracy of the model is less, the speed of detection is extremely high. So based on our research we have figured out the best performance object detection techniques and also the most accurate technique. A well-trained object detection model must be very optimistic in terms of their speed and accuracy.

Keywords: Object Detection, Deep Learning, R- Convolution Neural Network, Single Shot MultiBox Detector(SSD), You Only Look Once(YOLO).
Scope of the Article: Deep Learning