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A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.

Saydirasulov Norkobil Saydirasulovich1, Akmalbek Abdusalomov1, Muhammad Kafeel Jamil1

  • 1Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea.

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Summary

This study shows YOLOv6 effectively identifies fire-related items for improved fire detection and emergency response in Korea. The system demonstrates high accuracy and real-time performance, making it a viable tool for community safety.

Keywords:
YOLOv6deep learningfirefire image datasetflame detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Safety Technology

Background:

  • Korean authorities are prioritizing enhanced fire prevention and emergency response systems.
  • Automated fire detection and identification are key to improving community safety.
  • Evaluating advanced object identification systems is crucial for effective fire management.

Purpose of the Study:

  • To assess the efficacy of YOLOv6 for identifying fire-related items in Korea.
  • To analyze YOLOv6's performance metrics including speed, accuracy, and real-world applicability.
  • To compare YOLOv6 with other machine learning models for fire object recognition.

Main Methods:

  • Utilized YOLOv6, an object identification system on an NVIDIA GPU platform.
  • Trained and tested YOLOv6 on a dataset of 4000 fire-related images.
  • Compared YOLOv6 with Random Forests, k-NN, SVM, Logistic Regression, Naive Bayes, and XGBoost on SFSC data.
  • Evaluated performance using metrics like precision, recall, MAE, and response time.
  • Tested YOLOv6 in a simulated fire evacuation scenario.

Main Results:

  • YOLOv6 achieved an object identification performance of 0.98, with 0.96 recall and 0.83 precision.
  • The system demonstrated a Mean Absolute Error (MAE) of 0.302%.
  • XGBoost classifier showed the highest accuracy (0.717-0.767) for multi-class object recognition on SFSC data.
  • YOLOv6 accurately identified fire-related items in real-time within 0.66 seconds during simulated evacuations.

Conclusions:

  • YOLOv6 is a highly effective tool for real-time fire detection and identification in Korea.
  • The system's speed and accuracy support its integration into automated fire prevention strategies.
  • YOLOv6 offers a viable solution for enhancing public safety through advanced AI-driven fire recognition.