Object detection using machine learning for visually impaired people

Abstract

In this challenging evolution, the primary task in detecting the objects requires a computer vision that deals over indoor and outdoor classes. Over the past decades, this zeal requires more attentiveness. Previous implementation techniques involve in object detection with a strategy of single labelling. Aim and Objectives: In this regard, a multi-label approach using machine learning and vision technologies, and accurate response can be acknowledged based on its accuracy and effectiveness. In the proposed work, we solve the existing system problem by using classification/clustering techniques that are used to reduce the recognize time of multi objects in less time with best time complexities. Model: The model used to assist the visually impaired people can independently recognize objects which are near to them. The reverence, combined with the study, confounded the inception of these machine learning algorithms for visually impaired persons in assisting the accurate navigation, including indoor and outdoor circumstances. Conclusion: In this connection, an indoor and outdoor architecture on Retina Net is implemented for its detection techniques, and also neural network technologies support this framework. Based on the effectiveness and implementation time, ResNet and FPN act as a crucial module for its accuracy.