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By: Ravindaran F, Dhinesh K, Dhinesh C, Gowrishankar S, and Ashokkumar S.
1-5Student, Department of Computer Science and Engineering (Cyber Security), Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
This paper brings forth state-of-the-art blind assistance, and the system here provides a combination of an OpenCV-based DNN module based on YOLOv3’s algorithm; the system would support real-time object detection, with associated auditory notifications to enable visually impaired individuals to understand their autonomy couched with safety about improved fast recognition of objects allied with auditory responses. The live video captures from a webcam were done and further processed by the YOLOv3 algorithm. Optimize to work in real time for accurate object identification. Relayed objects are then passed on to the user through voice alerts generated using GTTS technology, thus passing on all the relevant information in an audio format. Another intuitive aspect of the system is that it is adaptive. It may alter the voice outputs so that they would be in the language of preference to the user. It is easier and more accessible to use it. In addition, the precision of the system in the recognition of large categories of objects makes it very versatile and, therefore, very valuable in making life quality enhancement for visually impaired people.
YOLOv3 (you only look once), DNN (deep neural network), GTTS (google text-to-speech), OpenCV, visually impaired
Citation:
Refrences:
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