A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

Authors

  • S. Sajini Department of Computer and Information Science, Faculty of Science, Annamalai University, India
  • B. Pushpa Department of Computer and Information Science, Faculty of Science, Annamalai University, India
Volume: 14 | Issue: 1 | Pages: 12716-12721 | February 2024 | https://doi.org/10.48084/etasr.6631

Abstract

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.

Keywords:

binary object detection pattern model, KERAS dataset, visually impaired, computer vision, camouflaged object detection, parameter assessment

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How to Cite

[1]
S. Sajini and B. Pushpa, “A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12716–12721, Feb. 2024.

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