AI-Powered Smart Glasses Using the ESP32 Processor

References

[1] Das S, Saxena S, Rout NK. Low cost smart-glass using ESP32. In: Proceedings of the IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC); 2021; Bhubaneswar, India.

[2] Lin JY, Yao CC, Chiang CL, Chen MC, Wu MJ. Smart glasses application system for visually impaired people based on deep learning. In: Proceedings of the Indo-Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN); 2020. p. 202–206.

[3] Uma R, Harish PS, Ragav NG, Navin A. Third Eye – A smart wearable glass with deep learning technology powered with artificial intelligence for visually impaired people. In: Proceedings of the IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS); 2023; Bhopal, India.

[4] Ramesh Babu BSSV, Jayakrishna G, Mahendra B, Gowtham M, Manasa ALSS, Chandrakanth M. AI-powered smart glasses for the blind using OpenCV & Python. In: Proceedings of the IEEE Wireless Antenna and Microwave Symposium (WAMS); 2025; Visakhapatnam, India.

[5] Sharma P, Babu AS, Sadasivan M, Aroan Robert KT, Vasumathi MT, Ashok Kumar TA. Smart glasses for the blind: A real-time AI-driven wearable system for autonomous navigation. In: Proceedings of the International Conference on Computing and Communications (COMPUTINGCON); 2025; Pune, India.

[6] World Health Organization. Blindness and vision impairment [Internet]. Fact sheet; 2023. https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment

[7] Malhotra S, Prasad M, Vashist P, Kalaivani M, Gupta SK. Prevalence of blindness in India: A systematic review and meta-analysis. The National Medical Journal of India. 2019;32:325–333.

[8] Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016; Las Vegas, NV, USA. p. 779–788.

[9] Redmon J, Farhadi A. YOLOv3: An incremental improvement [Preprint]. arXiv:1804.02767; 2018.

[10] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2001.

[11] Viola P, Jones MJ. Robust real-time face detection. International Journal of Computer Vision. 2004;57:137–154.

[12] Chen J, Ran X. Deep learning with edge computing: A review. Proceedings of the IEEE. 2019;107(8):1655–1674.

[13] Groq Inc. Groq API documentation [Internet]. 2024. https://console.groq.com/docs

@article{1724507,
      author = {Asst. Prof. Shibu Chacko, Harinand J, Arya B Shetty, Karthik P, Pavan S N},
      title = {AI-Powered Smart Glasses Using the ESP32 Processor},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {111-120},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724507.pdf},
      abstract = {Vision impairment affects over 2.2 billion people worldwide and continues to impose severe barriers to independent daily life, from safe outdoor navigation to reading signage and recognising familiar faces. Conventional aids such as the white cane provide limited scene awareness and cannot describe objects, read text, or interpret traffic signals. The convergence of affordable edge-computing hardware, mature deep learning frameworks, and free-tier cloud AI services has spawned a new generation of AI-powered smart glasses that begin to close this gap. This paper presents a structured comparative review of five peer-reviewed conference publications spanning 2020 to 2025, each proposing a working smart-glasses prototype for blind and visually impaired users. The systems surveyed range from a minimal ESP32-based image-relay prototype to a classical Viola-Jones OpenCV pipeline, a YOLOv3 client-server deep learning design, a YOLOv5 and Kalman-filter sensor-fusion wearable, and a Google Coral Edge TPU driven autonomous navigation system. Each design is analysed across hardware platform, detection algorithm, sensing modalities, user feedback channel, and quantitative performance where available. A cross-paper comparison is supported by a generalised four-layer architectural model, a taxonomy of detection algorithms, and a hardware evolution timeline. The survey concludes with a discussion of open challenges and concrete future directions including large language model integration and hybrid cloud-edge processing.},
      keywords = {smart glasses; visually impaired; assistive technology; ESP32-CAM; object detection; YOLO; edge AI; Coral TPU; sensor fusion; wearable computing; deep learning; computer vision},
      month = {November},
  }