DEEP LEARNING FOR VISUAL RECOGNITION

Abstract

The design and development of an advanced object detection system are presented in this work, which was guided by a thorough literature review and feasibility assessment. The literature review emphasises how object detection techniques have evolved, highlighting the shift from conventional techniques to deep learning approaches. Important developments are highlighted, such as feature pyramid networks, anchor-based localization, and region-based and single-stage detectors. Furthermore, offered are insights about assessment metrics, transfer learning, and data augmentation. A feasibility study assesses the suggested systems operational, technological, and financial viability and finds that it is highly feasible in each of these areas. The architecture of the system is modular and scalable, including backend services, data management, an object detection engine, and a user interface among its constituent parts. Specifications for both functional and non-functional needs are provided, which direct the development of the system. The development phases, resource allocation, development process, and quality assurance procedures are all outlined in the implementation plan. Through the integration of deep learning techniques, the suggested system seeks to achieve high-performance object identification capabilities that are appropriate for a variety of applications while being scalable, reliable, and user-friendly.