Скачать или смотреть Speed Bump Detection for Enhanced Driving Safety using Deep Learning | Python AI IEEE Project 2025

Speed Bump Detection for Enhanced Driving Safety using Deep Learning | Python AI IEEE Project 2025 - 2026.
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🌐Website: https://www.jpinfotech.org

📌Our Proposed Project Title: Speed Bump Detection for Enhanced Driving Safety using Deep Learning.
💡Implementation: Python.
🔬Algorithm / Model Used: YOLOv11 architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/

📘Project Abstract:
👉“Speed Bump Detection for Enhanced Driving Safety using Deep Learning” system is implemented using Python as the core programming language, with HTML, CSS, and JavaScript for the front-end interface and Flask as the web framework to integrate the detection engine with user interaction modules.
👉The proposed approach employs the YOLOv11 architecture for efficient object detection, enabling fast and precise localization of speed bumps in road scenes.
👉The model is trained and evaluated on a custom dataset consisting of 1,927 training images.
👉The system supports two operational modes: Image Mode and Live Webcam Mode.

🚀IEEE Base Paper Title: Speed Bump Detection Model for Advanced Driver Assistance System.

📍REFERENCE:
K Gopala Sharma, Nirmala Paramanandham, “Speed Bump Detection Model for Advanced Driver Assistance System”, IEEE Conference 2025.

🕑 Timeline:
00:12 - Intro.
00:38 - IEEE Base Paper Concept.
02:41 - Our Proposed Project Abstract.
03:34 - Dataset Details.
06:49 - Existing System.
07:58 - Proposed System.
09:19 - System Architecture.
09:45 - System Requirements.
10:43 - Project Execution.
24:07 - Frequently Asked Questions (FAQ's)

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