An Intelligent Object Detection and Recognition Framework for Assisting the Visually Impaired

In this paper, the design, development, and evaluation of an intelligent object detection and recognition system are described to enhance the mobility and environmental awareness of visually impaired people. To recognise objects, the system uses more sophisticated deep learning models, such as YOLOv5 and an optimally installed hardware-software platform of high accuracy and low-latency real-time object detection. That is, the system transforms optical visual data semantically to sound data. In testing the performance of the designed assistive system, the performance tests also involved extensive performance tests on the new assistive system, which concluded that the new assistive system was superior to the benchmark assistive systems in areas such as accuracy, precision, and responsiveness. The qualitative user reviews on the system state that the system was assisting in enhancing the self-confidence, ease and practical advantages to the user to navigate easily in real life. It shows how far assistance systems have gone, even though the system has drawbacks related to energy consumption, interface design, accommodating the system to the environmental changes of the user, among other changes in the system. The proposed system illustrates how an AI-driven visual system can be used to encourage the autonomy and self-sufficiency of users with visual taken advanced visual systems with assistive technology.