International Journal of Science, Engineering and TechnologyAuthors: Mr. Vaibhav Shrivastava, Nishchay Sakure, Chanchal Pagare, Md. Eliyas Sheikh, Sapna Debnath, Saurabh Sharnagat, Harsha Keche
Abstract: BlindAssist is a Python prototype that uses a laptop webcam and a YOLOv8 object detector to tell a visually impaired user, by voice, which objects are in front of the camera and roughly where they are. Frames are captured with OpenCV and passed to a COCO-pretrained YOLOv8 model (Ultralytics), and the results are spoken offline with pyttsx3. Each announcement gives the object’s class name and whether it lies to the left, ahead, or to the right, based on the horizontal position of its bounding box. For seven object classes an uncalibrated estimate of distance is computed from the box width and shown on screen; it is not spoken. Objects are tracked by class name, so that a class is announced when it first appears, when its direction changes, and once when it has been missing for three consecutive detection passes. Face modules using YuNet and SFace (OpenCV) and the DeepFace library recognize known people from reference photographs, and a DeepFace emotion model announces the dominant facial expression of each detected face; a custom pen-and-pencil model is under development. The paper describes the design, gives the algorithms and configuration, compares the system with published assistive systems, and describes the tests we plan to run. No labeled test set or user study has been completed, so no accuracy, speed, or user-study figures are reported.
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