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Phoenix Precision Algorithm for Blind People With Enhanced Voice Assistant
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Author(s): Judy Flavia B. (SRM Institute of Science and Technology, India), S. Sridevi (SRM Instıtute of Science and Technology, India), V. Srivathsan (SRM Instıtute of Science and Technology, India), Aravindak Kumar R. K. (SRM Instıtute of Science snd Technology, India), Ashwin Kumar M. K. (SRM Instıtute of Science and Technology, India), S. Rubin Bose (SRM Instıtute of Science and Technology, India)and R. Regin (SRM Instıtute of Science and Technology, India)
Copyright: 2024
Pages: 18
Source title:
Advanced Applications of Generative AI and Natural Language Processing Models
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Bharat Bhushan (School of Engineering and Technology, Sharda University, India), Muthmainnah S. (Universitas Al Asyariah Mandar, Indonesia)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-0502-7.ch016
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Abstract
The chapter presents an innovative approach to object detection that combines the advantages of the DETR (DEtection TRansformer) and RetinaNet models and features a phoenix precision algorithm. Object tracking is a basic computer vision task for identifying and locating objects in an image. The DETR model revolutionized object detection by introducing a transformer-based architecture that eliminates the need for anchor boxes rather than maximum damping, resulting in industry-leading performance. On the other hand, RetinaNet is a popular single-stage object detection model known for its efficiency and accuracy. This chapter proposes a hybrid model that uses both DETR and RetinaNet. The transformer-based architecture of the DETR model provides an excellent understanding of the overall context and allows you to capture long-range dependencies and maintain object associations. Meanwhile, RetinaNet's pyramid array (FPN) and focus loss enable precise localization and manipulation of objects at different scales.
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