Keywords – Retina Net Algorithm, Object Detection, Python, Deep Learning.
Now a days, there are a number of disabled people, specifically, partial vision around the world needs help through using available technology. Partial vision occurs when a human unable to see normally or partial loss of vision. In  stated that, most of partial vision people have come across embarrassing moments in between societies due to their lack of vision. And this loss is making their daily life harder than normal human beings. In this project, the aim is to use available technologies to create and develop a way to help them with detecting some objects throughout their daily activities. Machine Learning is the approach of this project.
In , it explains how in recent years, laptop vision technologies particularly the deep convolutional neural network have been quickly created. By using laptop vision strategies, it will be easy to help individual people with vision loss. We would like to discuss the idea of using the sense of hearing to detect visual objects during this project. We might be able to detect the object with a background voice. In , deformable elements model which uses the root filters to detect the whole image in a window and CNN layers will help us for bounding boxes to classify. Retina net model has been contrasted and differed elective models. In  shows Retina net model appearance all out picture at take a glance at time accordingly its expectations and are edified by world setting. It makes expectations with one system examination. Here item location might be a relapse downside to spatially isolated bouncing boxes and related classification possibilities.
Having a goal to help human is the best choice to success. This project focuses on using object detection along with Retina Net Algorithm for detecting a number of objects that might be helpful to partial vision patients during their daily activities. Most important, we will have the opportunity to learn Deep Learning technology as students.
 Michaels, E. (2021, February 04). Finding happiness through pain and embarrassment. Retrieved February 26, 2021, from https://www.audible.com/pd/B08VTVNDYL
 Thatti, H. R. (n.d.). Real Time Multi-Object Identification Using RetinaNet. Retrieved February 25, 2021.
 P. F. Felzenszwalb, R. B. Girshick, D. McAllester and D. Ramanan, “Object Detection with Discriminatively Trained Part-Based Models,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, Sept. 2010, doi: 10.1109/TPAMI.2009.167.
 T. Lin, P. Goyal, R. Girshick, K. He and P. Dollár, “Focal Loss for Dense Object Detection,” 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 2999-3007, doi: 10.1109/ICCV.2017.324.
Course: Python/DL Programming
Team Members (04)
China Venkat Chowdary Arikatla – firstname.lastname@example.org
Venkata Rahul Torlikonda – email@example.com
Ahmed Alanazi – firstname.lastname@example.org
Rasheed Alhazmi – email@example.com