Smart Blind Glasses Using OpenCV Python

• Published in: 2024 IEEE Wireless Antenna and Microwave Symposium (WAMS) pp. 01 - 04
• Main Authors: Babu, B.S.S.V.Ramesh, Basha, Shaik Dall, Chandrakanth, Munubarthi, Teja, Reddi Tanay, Hemanth, Pasala
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.02.2024
• Subjects: Arduino IDE; Computer vision; Internet of Things (IOT); Location finder (GPS); Microwave antennas; Nose; OpenCV' ESP32 Camera; Technological innovation; Visual impairment; Visualization; Wireless communication
• DOI: 10.1109/WAMS59642.2024.10527868

With the continuous evolution of technology, the field of object detection has witnessed significant progress. Early techniques relied on hand-crafted features and less precise algorithms, often requiring additional computer vision methods for support. This reliance, however, resulted in suboptimal performance and slow processing speeds. In response to these challenges, this project introduces an end-to-end solution to object detection utilizing an OpenCV Python-based approach. The innovation extends beyond mere visual recognition by incorporating an ESP32 module to convey the detected objects audibly, addressing the accessibility needs of visually impaired individuals. The proposed framework not only streamlines the object detection process but also extends its functionality to identify specific facial features, such as eyes and nose. By eliminating the dependency on external computer vision methods, the system achieves improved efficiency and accuracy. The integration of audio output through the ESP32 module enhances the project's impact, making the information accessible to users with visual impairments. The implementation demonstrates the potential of combining computer vision with versatile hardware solutions for a more inclusive and efficient object detection system.