AI-Based Smart Stick for the Visually Impaired
DOI:
https://doi.org/10.61779/jasetm.v3i2.1Keywords:
Smart blind stick, object detection, assistive technology, YOLO, ESP32Abstract
Visually impaired individuals face numerous challenges while navigating their environment. Traditional mobility aids, such as white canes, provide limited assistance in detecting obstacles. To enhance their independence and safety, smart assistive technologies are needed. This paper presents a Smart Blind Stick designed to assist visually impaired individuals by identifying objects in their surroundings and providing real-time audio feedback. The system integrates an ESP32 camera module and a Raspberry Pi, utilizing YOLO (You Only Look Once) object detection for image processing. The ESP32 is mounted on the blind stick to capture real-time images, which are transmitted to the Raspberry Pi for analysis. Upon detecting and identifying objects, the system generates an audio message and conveys it to the user via headphones, enhancing their awareness of obstacles and surroundings. This innovation aims to improve mobility, safety, and independence for the visually impaired through affordable and efficient technology.
References
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” Apr. 2020. https://arxiv.org/abs/2004.10934
G. Jocher et al., “YOLOv8: State-of-the-art object detection, segmentation, and classification,” Jan. 2023. https://ultralytics.com/yolov8
Espressif Systems, ESP32 Technical Reference Manual, 2023.: https://www.espressif.com/sites/default/files/documentation/esp32_technical_reference_manual_en.pdf
Raspberry Pi Foundation, Raspberry Pi Documentation, 2023. https://www.raspberrypi.com/documentation/microcontrollers/
A. Alreshidi, “Assistive technology acceptance for visually impaired individuals: A case study of students in Saudi Arabia,” PeerJ Computer Science, vol. 8, p. e886, Jul. 2022. Taylor & Francis. https://peerj.com/articles/cs886/
F. Roboflow Team, “RF-DETR: Real-time Detection Transformer achieves 60+ mAP on COCO,” Roboflow Blog, Feb. 2025. link.springer.com+6sciencedirect.com+6sciencedirect.com+6
Z. Li and X. Wang, “Small Object Detection: A Comprehensive Survey on Challenges, Datasets, and Methods,” arXiv, Mar. 2025.
J. Khoramdel, A. Moori, Y. Borhani, A. Ghanbarzadeh, and E. Najafi, “YOLO Former: YOLO Shakes Hand with ViT,” arXiv, Jan. 2024.
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Copyright (c) 2025 Alen Manoj, Madhu Amal A, Shaun Mathew, Vishwam Sajeev, Ambily John

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