โ12 readsโCreative Commons Attribution-NonCommercial (CC BY-NC)
๐ป๐๐๐๐๐๐ ๐ฐ๐๐๐๐๐ ๐๐๐๐ ๐น๐๐๐-๐พ๐๐๐๐ ๐จ๐๐๐๐๐ ๐๐๐๐ ๐๐ถ๐ณ๐ถ๐11 ๐๐๐
Table of contents
Title
๐ป๐๐๐๐๐๐ ๐ฐ๐๐๐๐๐ ๐๐๐๐ ๐น๐๐๐-๐พ๐๐๐๐ ๐จ๐๐๐๐๐ ๐๐๐๐ ๐๐ถ๐ณ๐ถ๐11 ๐๐๐ ๐จ๐๐ ๐๐๐๐! ๐๐ฅ
Introduction
Today, I took a giant leap in ๐๐๐๐ ๐๐๐๐ ๐๐๐ ๐๐๐ ๐๐๐๐๐๐๐ ๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐ ๐๐๐๐-๐๐๐๐๐ ๐๐๐๐๐๐๐. By training Ultralytics ๐๐ถ๐ณ๐ถ๐11 model for fire ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง and integrating it with an Arduino, I created a system that not only detects fire in real-time but also triggers physical responses, like turning on a buzzer and LED. ๐ฅ๐ค
๐ข ๐๐๐ฒ ๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ๐ฅ ๐๐ฎ๐ฌ๐ญ๐จ๐ฆ ๐ ๐ข๐ซ๐ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง ๐ฐ๐ข๐ญ๐ก ๐๐๐๐๐ฏ๐๐: Trained a custom ๐๐ถ๐ณ๐ถ๐11 model specifically for fire detection, ensuring high accuracy and precision in recognizing fire in dynamic real-world environments.
โ ๐ฅ๏ธ ๐๐๐๐ฅ-๐๐ข๐ฆ๐ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง: Used ๐ถ๐๐๐๐ช๐ฝ to capture webcam frames, passing them through the ๐๐ถ๐ณ๐ถ๐11 model to detect fire with high confidence and speed.
โ ๐ค ๐๐ซ๐๐ฎ๐ข๐ง๐จ ๐๐ง๐ญ๐๐ ๐ซ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ซ ๐๐๐ญ๐ข๐จ๐ง: Seamlessly integrated the detection system with ๐จ๐๐
๐๐๐๐, enabling real-time control of physical hardware:
โข ๐ ๐๐ฎ๐ณ๐ณ๐๐ซ & ๐๐๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: When fire is detected, the system triggers the buzzer and LED, providing immediate feedback and enhancing safety measures.
โข ๐ ๐๐ฆ๐๐ซ๐ญ ๐๐จ๐ฆ๐ฆ๐๐ง๐ ๐๐๐ง๐๐ฅ๐ข๐ง๐ : The system listens for and sends commands like ๐ญ๐๐๐ or ๐บ๐๐๐ based on ongoing detection, ensuring precise control over the hardware.
โ ๐ง ๐๐๐ซ๐ฌ๐ข๐ฌ๐ญ๐๐ง๐ญ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ ๐ข๐: Developed a system where the ๐ญ๐๐๐ command is triggered only after continuous detection for a set number of frames, and the ๐บ๐๐๐ command is sent after no detection for a specified threshold.
๐ ๐๐ก๐๐ญ ๐ ๐๐๐๐ซ๐ง๐๐:
โ ๐ค ๐๐ฎ๐ซ๐ง๐ข๐ง๐ ๐๐ ๐ข๐ง๐ญ๐จ ๐๐๐ญ๐ข๐จ๐ง: ๐๐ถ๐ณ๐ถ๐11 transforms digital insights into physical actions, enhancing fire detection's interactivity.
โโก ๐๐๐๐ฅ-๐๐ข๐ฆ๐ ๐๐ฒ๐ฌ๐ญ๐๐ฆ ๐ ๐๐๐๐๐๐๐ค: AI-hardware integration enables seamless, safety-focused automation.
โ ๐ ๐๐ก๐ ๐๐จ๐ฐ๐๐ซ ๐จ๐ ๐๐๐ญ๐ ๐๐๐ง๐๐ ๐๐ฆ๐๐ง๐ญ: Handling real-time communication between the detection system and hardware is a delicate balance of precision and error handling, which is essential for ensuring reliability.
๐ ๐๐ก๐๐ญโ๐ฌ ๐๐๐ฑ๐ญ:
โ ๐ก ๐๐ฑ๐ฉ๐ฅ๐จ๐ซ๐ข๐ง๐ ๐๐๐ฐ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ: Expanding AI-driven fire detection to smart homes and industrial safety.
โ ๐ฌ ๐๐ฒ๐ฌ๐ญ๐๐ฆ ๐๐๐ฌ๐ญ๐ข๐ง๐ : Enhancing robustness by testing in diverse environments.
Conclusion:
This project represents a significant step in harnessing the power of AI for real-world safety applications. By combining YOLOv11โs advanced fire detection capabilities with Arduino's hardware control, Iโve demonstrated how digital intelligence can drive physical action to create a smarter, safer environment. This seamless integration opens new possibilities for AI-driven safety measures in homes and industries alike.
Looking ahead, thereโs immense potential to refine and expand this system for broader applications, including real-time monitoring and autonomous response systems. Itโs exciting to envision a future where technology like this becomes a cornerstone of proactive safety solutions.
