Object Detection and Classification for Autonomous Drones
Harit Ahuja1, Vedant Kuhar2, R. I. Minu3
1Harit Ahuja, Computer Science and Engineering Department, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
2Vedant Kuhar, Computer Science and Engineering Department, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
3DR. R.I. Minu Computer Science and Engineering Department, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3165-3169 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8862038620/2020©BEIESP | DOI: 10.35940/ijrte.F8862.038620
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© 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: Detecting and classifying objects in a single frame which consists of several objects in a cumbersome task. With the advancement of deep learning techniques, the rate of accuracy has increased significantly. This paper aims to implement the state of the art custom algorithm for detection and classification of objects in a single frame with the goal of attaining high accuracy with a real time performance. The proposed system utilizes SSD architecture coupled with Mobile Net to achieve maximum accuracy. The system will be fast enough to detect and recognize multiple objects even at 30 FPS.
Keywords: SSD; FPS; Mobile Net; Tensorflow
Scope of the Article: Classification.