AI

Industrial safety

Structural safety built by technology — the standard for AI industrial safety

OVERVIEW

Instead of finding the cause after an accident,the dangerous moment is stopped where it happens

Safety management moves from experience and instinct to judgment read out of video and sensor data.

  • Figurines of workers in hard hats standing on industrial equipment

    01Reliance on experience and instinct

    Safety management that depends on a worker's intuition and experience is limited in carrying out objective risk assessment and continuous preventive activity.

  • Illustration of people standing before a law book and a gavel

    02Failure to meet serious-accident liability law

    If an accident occurs at a workplace that has not established a safety and health management system, the responsible executive becomes subject to criminal penalties.

  • A damaged hard hat abandoned on bare ground

    03Weak recurrence-prevention system

    With too little accumulated accident-analysis data, fundamental countermeasures are hard to establish, creating a vicious cycle in which similar accidents repeat.

KEY FEATURES

Four capabilities that read risk on the floor

From object and behavior recognition through compound hazard detection and collision prevention to model optimization.

Safety Capability

01 / 04

01

Object and behavior recognition

It identifies workers, equipment and hazardous materials on the industrial site in real time and analyzes their movement. Overcoming the limits of human vision, it provides monitoring with no blind spots and judges hazardous situations the moment they arise, preventing safety accidents.

Management Scope

  • Multi-object tracking
  • Abnormal behavior learning
  • Edge AI processing
  • Real-time hazard detection
  • Collision prediction algorithm
  • Site-specific training
AI monitoring screen tracking workers and forklifts in warehouse footage with bounding boxes and movement paths

Object and behavior recognition

CCTV footage detects in real time whether workers are wearing helmets, entering hazardous zones or behaving unusually

WORKFLOW

The workflow behind each capability

For each capability, the order it runs in and the check it passes before moving on.

Defining risk and monitoring scope per process

  1. 01

    Site check

    press · robot · forklift

  2. 02

    Zone setup

    work · access · passage zones

  3. 03

    Video linkage

    check occlusion · lighting · blind spots

  4. 04

    Register standard

    risk condition/responsible party

Recorded alongside

01

Camera/hazard zone

02

Work/equipment state

03

Alert threshold/owner

Operating check

Can the actual hazard zone be observed?

YES

Monitoring scope → apply standard

NO

Blind spot → add camera/sensor

An example workflow · the detailed links and criteria are set to suit your floor.

SYSTEM ARCHITECTURE

What it connects,and the work it leads to

From the data that goes in to the results on the floor.

01 / INPUT

01Site videoCameras · workers · vehicles
02Hazard zonesEquipment areas · walkways · no-go lines
03Auxiliary signalsAccess · sensors · equipment state

CONNECTED OPERATIONS

AI Safety

Recognize people, equipment and actions
Distance · direction · hazard events
Warnings · response · review history

03 / OUTPUT

✓Hazard statusLocation · type · time
✓Instant alertsControl room · on-site alarms
✓Response recordCheck · action · recurrence review
Linked by common identifiers, master data and historyResults checked → fed into the next run
Who uses itSafety managersSite operatorsEquipment engineers

METHOD

How it is built

Site video and sensors gathered and judged, then returned as an alert or a control.

Collection01 →

Data Ingestion

CCTV

IP camera / RTSP

Wearables

Smart helmet / band

Environmental sensors

Gas / temperature / noise

Video framesLocation / vital signsGas / fireAccess / PTW
Signal processingSTFT / FFT

Frequency analysis · denoising · normalization

Time syncNTP/PTP

Maps timestamps to equipment history

Target System

Video storage
Time-series DBInfluxDB, Timescale
MetadataIncident history
Modeling & analysis02 →

AI/ML Modeling

Feature extraction & preprocessing

Behavior / postureAction recognition from video
Environment / fireCNN, XGBoost
Risk predictionCollision and pinch risk ahead

Analysis & prediction

Video preprocessing

Lighting correction, resolution normalization

Object detection

YOLO v10, 11

Pose estimation

Unsafe posture detection

Location estimation

Worker and heavy-equipment tracking

Service03

Operational visibility

Integrated safety monitoring
Worker location tracking

Instant hazard alerts

Early response, as it happens

Incident management & control

Serious-accident response
Safety compliance

Prevent serious accidents on the work site and build a safe working environment.

  • Construction workers on a steel frame at sunset

    Construction sites

  • Manufacturing site

    Manufacturing sites

  • Gas and chemical plant site

    Gas / chemicals

  • Logistics warehouse site

    Logistics warehouses

  • Fire extinguisher standing against a wall

    Fire prevention

  • Security monitoring site

    Security monitoring

An accident is flagged at the dangerous moment, not after it.

  • CCTV footage
  • Access and occupancy
  • Equipment running state
  • Work permits
  • Near-miss history
AI Safety
  1. Restricted area intrusion detection
  2. Protective equipment check
  3. Risk grading
  • Serious accident risk
  • Unprotected work
  • Response time
  • Safety check delivery

EFFECT

Business impact

What it leaves behind — in serious-accident risk, unprotected work, response time and inspection compliance.

  • A 3D illustration linking site workers, control screens, and detection gear to a holographic globe marked with a safety shield
  • 01

    Lower serious-accident risk

    Entry into a danger zone and the first sign of failure are caught in real time and stopped before they become an accident.

    Accidentszone entryfailure signs
  • 02

    Less unprotected work

    Missing protective equipment and skipped procedure are judged from video and warned on the spot, so no task runs unprotected.

    PPEprocedureinstant warning
  • 03

    Faster response

    An incident is detected automatically and pushed straight to the person responsible, with the location and the steps to take.

    Detectionnotificationprocedure
  • 04

    Higher inspection compliance

    Inspection history and proof of duty accumulate on their own, so the compliance rate the law asks for can be checked at any time.

    Recordsduty of carecompliance rate

CASE STUDY

Case study

A car-parts plant where press lines, robot cells and forklift routes overlap, watched in real time by vision AI.

A car-parts plant with press lines and robot cells

Customer industry

Car-parts manufacturing (pressing and welding)

  • Heavy material handling
  • Robot cells in operation
  • Forklift routes crossing

Before01

The company

  • 500,000 body parts a year
  • Press, robot and forklift routes overlap
  • Blind spots in CCTV monitoring

After02

What was built

  • 40 vision-AI cameras over the risk zones
  • Equipment halts on zone entry
  • Missing gear caught within 0.5s

What it produced

  • 94% more incident signs caught early
  • 99% protective-gear compliance
  • Records for safety-law compliance

Visual monitoring replaced by real-time AI detection