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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16698 |
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| _version_ | 1866914001142677504 |
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| author | Chandra, Sunkalp Sharma, Umang Khilnani, Devesh |
| author_facet | Chandra, Sunkalp Sharma, Umang Khilnani, Devesh |
| contents | Visual impairment impacts more than 2.2 billion people worldwide, and it greatly restricts independent mobility and access. Conventional mobility aids - white canes and ultrasound-based intelligent canes - are inherently limited in the feedback they can offer and generally will not be able to differentiate among types of obstacles in dense or complex environments. Here, we introduce the IoT Cane, an internet of things assistive navigation tool that integrates real-time computer vision with a transformer-based RT-DETRv3-R50 model alongside depth sensing through the Intel RealSense camera. Our prototype records a mAP of 53.4% and an AP50 of 71.7% when tested on difficult datasets with low Intersection over Union (IoU) boundaries, outperforming similar ultrasound-based systems. Latency in end-to-end mode is around 150 ms per frame, accounting for preprocessing (1-3 ms), inference (50-70 ms), and post-processing (0.5-1.0 ms per object detected). Feedback is provided through haptic vibration motors and audio notifications driven by a LiPo battery, which controls power using a PowerBoost module. Future directions involve iOS integration to tap into more compute, hardware redesign to minimize cost, and mobile companion app support over Bluetooth. This effort offers a strong, extensible prototype toward large-scale vision-based assistive technology for the visually impaired. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Computer Vision and Depth Sensor-Powered Smart Cane for Real-Time Obstacle Detection and Navigation Assistance for the Visually Impaired Chandra, Sunkalp Sharma, Umang Khilnani, Devesh Other Quantitative Biology Visual impairment impacts more than 2.2 billion people worldwide, and it greatly restricts independent mobility and access. Conventional mobility aids - white canes and ultrasound-based intelligent canes - are inherently limited in the feedback they can offer and generally will not be able to differentiate among types of obstacles in dense or complex environments. Here, we introduce the IoT Cane, an internet of things assistive navigation tool that integrates real-time computer vision with a transformer-based RT-DETRv3-R50 model alongside depth sensing through the Intel RealSense camera. Our prototype records a mAP of 53.4% and an AP50 of 71.7% when tested on difficult datasets with low Intersection over Union (IoU) boundaries, outperforming similar ultrasound-based systems. Latency in end-to-end mode is around 150 ms per frame, accounting for preprocessing (1-3 ms), inference (50-70 ms), and post-processing (0.5-1.0 ms per object detected). Feedback is provided through haptic vibration motors and audio notifications driven by a LiPo battery, which controls power using a PowerBoost module. Future directions involve iOS integration to tap into more compute, hardware redesign to minimize cost, and mobile companion app support over Bluetooth. This effort offers a strong, extensible prototype toward large-scale vision-based assistive technology for the visually impaired. |
| title | A Computer Vision and Depth Sensor-Powered Smart Cane for Real-Time Obstacle Detection and Navigation Assistance for the Visually Impaired |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2508.16698 |