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Solution

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FIREWATCHER is a smart wildfire smoke detection solution to analyze
multi-channel CCTV videos with the use of deep-learning based video analysis technology
and to take initial measures for extinguishing wildfire whose smoke is detected in CCTV videos.

Expectancy effect

  • Applied technology
    • Simultaneous analysis on multi-channel CCTV videos
    • Deep-learning model with the dataset of Korean mountain conditions
    • Division into a total of 10 detection classes including smoke, fog, and cloud
  • Work / Function
    • Short messaging service (SMS) when wildfire smoke is detected
    • 24-hour real-time monitoring through CCTV video analysis
    • Estimation of the real position of the smoke detection area in video
  • Management efficiency
    • System state monitoring
    • Event history and statistics
    • CCTV and admin (for SMS) information registration

Main functions

  • Multi-channel
    CCTV video analysis

  • AI based object
    identification and recognition
    (smoke, cloud, and
    steam classification)

  • Real-time alarms
    for events

  • Immediately initial
    measures for
    fire/wildfire

Solution features

  • 01

    Early detection of fire through the detection of ‘smoke’, rather than ‘fire’

  • 02

    Deep-learning model of minimizing errors, such as the detection of cloud and fog looking like smoke

  • 03

    Real-time simultaneous analysis and monitoring of multiple channels

Solution environment

S/W spec.

Server
OS : Ubuntu 20.04 LTS
DB : MongoDB v3.6.3
Client
OS : Windows 10 Pro 64 bit
Browser : Chrome 102.0 or higher

H/W spec.

Server
CPU : Intel(R) Xeon(R) Gold 6334 CPU@ 3.60GHz or more
GPU : Nvidia A10 (GPU Memory 24GB) or more
RAM : 64GB DDR4 2933Mhz or more

Client
CPU : Intel(R) Core(TM) i7-4702MQ CPU2.20 GHz or more
RAM : 8GB or more
HDD : 500GB or more

Solution components

Main display

  • Main display
    • CCTV channel and event information
    • Event statistics data
    • Display of public weather environment data
    • System sever operation information (CPU temperature)
    • Event & history management
    • Display of GIS based camera position information
  • Real-time monitoring for wildfire smoke detection
    • Minimization of errors through the division into a total of 10 classes including smoke, fog, and cloud
    • Deep-learning model with the dataset of Korean mountain conditions
    • 24-hour real-time monitoring through CCTV video analysis for smoke detection

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