Areeba Nasir · imagine-talent

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Academic

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Year

2021

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Career

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Skills

Deep learning, Computer vision, IOT, Android development

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.

RARroad is a computer vision based system that will assist the driver as road awareness is added in the rover using deep learning algorithms implemented on Raspberry Pi. The RAR system will detect and recognize traffic signs in real-time. The system will send its output to the mobile application that will generate alerts to inform the driver and then arduino will change the rover behavior according to the sign caught. It will significantly increase driver safety and road awareness. The objectives of RARroad are: 1. Create a lane following the rover. 2. Create an AI-based system that enables traffic sign detection and recognition in real-time. 3. Create an “Alert Generation System” using an android application that alerts and notifies the drivers whenever a traffic sign appears. Create an IOT based system that will change the rover behavior according to the sign caught. Technology Used: Deep learning, Computer vision, IOT, Android development. Supervisor Name: Dr. Adnan Tariq Group Members: Areeba Nasir (i17 - 0052)

AI enrichment

Areeba Nasir is a student who contributed to a computer vision project named RARroad, which utilizes deep learning and IoT to detect traffic signs and assist drivers. The system integrates Raspberry Pi, Arduino, and an Android application to provide real-time alerts and control rover behavior based on detected signs.

Skills (AI)

["Computer Vision", "Deep Learning", "IoT", "Android Development", "Raspberry Pi", "Arduino"]

Status: ai_done

Provenance

Source file: Graduate Directory FAST School of Computing 2021 (1st Final) (1).pdf
From job #24 page 227
Created: 1778144159