Enhancing safety of micro-mobility and powered mobility devices using YOLOv12-based real-time obstacle detection

Gunawan, Teddy Surya and Azlin, Amirul Aiman and Kartiwi, Mira and Md Yusoff, Nelidya (2025) Enhancing safety of micro-mobility and powered mobility devices using YOLOv12-based real-time obstacle detection. In: 11th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2025, 10-11 September 2025, Kuala Lumpur, Malaysia.

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Abstract

The increasing use of micro-mobility devices (MMDs) and powered mobility devices (PMDs) has improved personal mobility but raised safety concerns, particularly for elderly individuals and persons with disabilities who may have limited reaction times. This paper presents a real-time obstacle detection and alert system to enhance user safety without compromising manual control. The system integrates a Raspberry Pi 4 with a camera, MPU-6050 accelerometer, and buzzer, utilizing the YOLOv12 object detection model and DeepSORT tracking algorithm. Trained on 5,000 COCO images with 36,335 instances, the model achieved a precision of 0.601, a recall of 0.386, a mAP@50 of 0.419, and mAP@50–95 of 0.289. The model was converted to the NCNN format for lightweight deployment, enabling average inference at 214.3 ms per frame. Field tests across environments with minimal, moderate, and high obstacle densities validated the system’s real-time tracking, with DeepSORT reducing duplicate detections by over 40%. Audible alerts reliably notified users upon hazard detection. This work demonstrates the viability of embedding AI-powered safety systems on low-cost hardware for mobility applications. The prototype effectively enhances situational awareness, offering a scalable solution to reduce preventable accidents and support vulnerable user populations. Future work will address lighting variability and braking integration.

Item Type:	Proceeding Paper (Invited Papers)
Additional Information:	I am the first IIUM author. The paper has been recently indexed in Scopus https://www.scopus.com/pages/publications/105026946057
Uncontrolled Keywords:	Obstacle Detection, Powered Mobility Devices, Micro-Mobility Safety, Embedded Machine Learning, YOLOv12, DeepSORT
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	27 Jan 2026 16:21
Last Modified:	27 Jan 2026 16:21
Queue Number:	2026-01-Q1867
URI:	http://irep.iium.edu.my/id/eprint/127111

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