Fire and Smoke Detection Using Computer Vision
₹1,999.00
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- Source Code Included
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Detect fire and smoke automatically using computer vision and deep learning with this ready-to-run Python desktop application. The system uses a YOLO-based detection model to analyze images, recorded videos, and live webcam feeds, making it suitable for academic projects, demonstrations, research prototypes, and computer vision applications.
The application provides real-time visual detection with confidence controls, event logging, and audible alerts whenever fire or smoke is detected.
Key Features
- AI-Based Fire & Smoke Detection using YOLO
- Image Detection — analyze JPG, PNG, BMP, and WebP images
- Video Detection — process MP4, AVI, MKV, and MOV videos
- Live Webcam Detection — detect fire and smoke from a connected camera
- Real-Time Detection Overlay with bounding boxes and class labels
- Adjustable Confidence Threshold for detection sensitivity
- Automatic Fire/Smoke Status
- FIRE DETECTED
- SMOKE DETECTED
- FIRE & SMOKE
- Event Logging when fire or smoke is detected
- Audible Windows Alert using a short beep
- Alert Throttling to prevent continuous alarms on every frame
- Simple Tkinter GUI that is easy to operate
- Offline Processing — detection can run locally on the computer
- Easy Windows Setup using the included
run.bat
How It Works
The application loads the supplied YOLO model and analyzes the selected visual source.
Image → AI Detection → Fire/Smoke Identification → Bounding Box → Alert
For video and webcam sources, detection is performed continuously and the results are displayed directly on the video feed.
When the detected class contains “fire” or “smoke”, the application automatically updates the detection status, records the event in the log, and produces an audible alert.
Suitable For
This project can be used for:
- Engineering Projects
- Artificial Intelligence Projects
- Computer Vision Projects
- Fire Safety Monitoring Prototypes
- Industrial Safety Demonstrations
- Academic Mini Projects
Important Note
This is a computer vision project and prototype, not a certified fire alarm or life-safety system. Detection performance depends on the trained model, camera quality, lighting, scene conditions, and other environmental factors. It should not be used as the sole safety mechanism in critical fire-protection applications.
