Embedded Computer Vision for Object Recognition in Smart Devices for the Blind

This study has proposed a novel method for assisting the visually impaired people by combining computer vision with cutting-edge technological instruments. Convolutional Neural Networks (CNNs) are primarily utilized for performing realtime object recognition. To address the object identification challenge, this study collects and preprocesses a large dataset, construct an efficient CNN architecture, optimize inference speed, and connect this system to smart devices. The results indicate significant gains in precision and throughput for practical applications. This innovation may significantly enhance the quality of life for the visually impaired by increasing their mobility. The research study has far-reaching implications for assistive technology and sheds light on the possibility of making the world more accessible for those with visual impairments. Long-term, intends to enhance the consumer product by enlarging the dataset, refining the model's precision, and investigating additional features. This research is a crucial first step in utilizing technology to provide equal opportunities to individuals with visual impairments.