Phoenix Precision Algorithm for Blind People With Enhanced Voice Assistant

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.