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  Authors

V Prashanth,Sudeep Gouda S Patil,Sudeep AR,Yogesh j,Manjunth CR

  Keywords

Fire Detection Convolutional Neural Networks (CNN) Vision-Based Surveillance Image Processing Motion Detection Human-Animal Conflict Smart Agriculture YOLO Object Detection Real-Time Alerts Telegram Notification System Background Subtraction RGB and HSI Models Wildfire Monitoring Deep Learning for Fire Detection Precision Agriculture Forest Fire Prevention Animal Intrusion Detection Integrated Safety Systems AI in Agriculture Environmental Monitoring

  Abstract


Abstract-- Fire outbreaks are a common issue worldwide, causing significant damage to nature and human life. Modern fire detection solutions prioritize vision-based approaches over legacy sensor systems, enabling expansive monitoring and rapid threat identification in diverse environments. However, the detection process using image processing techniques can be complex and time- consuming. This paper proposes a Fire and Motion Detection System that utilizes Convolutional Neural Networks (CNNs) for high- accuracy fire detection. The system is trained on a dataset comprising 3,696 fire images and 541 non-fire images, totaling 4,237. Of these, 2,857 images were used for training and 1,921 images for testing. The CNN model employs convolution, activation functions, and max pooling operations. Through experimentation with different batch sizes and epoch values, the model achieved an accuracy of 94%, correctly predicting 1,817 out of 1,921 test images. Additionally, the system incorporates motion detection to address human-animal conflicts in agricultural fields. Using background subtraction and object classification (via YOLO or similar models), the system detects and classifies intruders (e.g., animals or humans) and sends real-time alerts to farm owners and officials via Telegram notifications. This study also reviews various smoke and fire detection techniques, including RGB and HSI models, to minimize false alarms and enhance early detection. The integration of fire and motion detection into a unified system provides a comprehensive solution for safeguarding agricultural fields and preventing disasters.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT25A4396

  Paper ID - 283424

  Author type - Indian Author

  Page Number(s) - l900-l906

  Pubished in - Volume 13 | Issue 4 | April 2025

  DOI (Digital Object Identifier) -   

  No Of Downloads - 69

  Author Country - India, 563114, Bangarpet , Bangarpet , 563114, Science and Technology

  Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT25A4396

  Published Paper URL: : - http://ijcrt.org/viewfull.php?&p_id=IJCRT25A4396

  Published Paper PDF Downlaod: - download.php?file=IJCRT25A4396

  Cite this article

V Prashanth,Sudeep Gouda S Patil,Sudeep AR,Yogesh j,Manjunth CR,   "Fire and motion detection system", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 4, pp.l900-l906, April 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A4396.pdf

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