Flag Semaphore Detection using Tensorflow and Opencv
Athul Motty1, Yogitha A2, R Nandakumar3

1.Athul Motty, Department of Computer Science and IT, Amrita School of Arts & Sciences, Kochi, Amrita Vishwa Vidyapeetham, (Tamil Nadu), India.
2Yogitha A, Department of Computer Science and IT, Amrita School of Arts & Sciences, Kochi, Amrita Vishwa Vidyapeetham, (Tamil Nadu), India.
3R Nandakumar, Department of Computer Science and IT, Amrita School of Arts & Sciences, Kochi, Amrita Vishwa Vidyapeetham, Coimbatore, (Tamil Nadu), India.

Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 508-512 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2472017519/19©BEIESP
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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: This paper studies the automatic recognition of Flag Semaphores. We consider both static semaphores wherein the flags are held by the signaler in fixed positions and also dynamic signaling with flags (used internationally for aircraft marshalling and also by mariners). Reading Static semaphores such as those used by mariners are our main focus. We suggest the use of image processing and machine learning techniques to recognize and detect the flags and the signaler. OpenCV technology was used to capture the images and the TensorFlow API to detect the static semaphores. We could achieve promising results in the detection of static flag semaphores – a confidence level of 99%. We conclude that for deciphering signals where the flags are in motion, more sophisticated machine learning methods would be needed.
Keywords: Image Processing, Flag Semaphore signals, Machine learning, Tensorflow API, OpenCV
Scope of the Article: Signal and Speech Processing.