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Intelligent crane safety-assist system integrating distance sensing, real-time camera monitoring, and scalable hardware architecture.

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Crane Sentia – Intelligent Crane Safety System

Crane-Sentia is a hardware-based smart safety system designed to enhance on-site crane operations using sensor-driven monitoring, distance detection, and real-time visual feedback. The word Sentia derives from Latin, meaning “sensing / awareness”, reflecting the system’s goal of enabling cranes to “sense” their surroundings. The project integrates multiple sensing modules to improve worker safety, reduce collision risks, and provide continuous situational awareness around crane movement areas.

System Overview

Modern construction sites involve heavy machinery, dynamic worker movement, and limited visibility. Crane-Sentia enhances safety by combining:

1. Distance Monitoring Module

Using ultrasonic sensors, the system measures the distance between the crane and nearby workers/objects, triggering alerts based on three safety stages: Safe – Attention – Dangerous

2. Visual Monitoring Module

An OV7670 camera module streams real-time frames to a connected laptop, providing visual confirmation of crane surroundings.

3. Weight Monitoring Architecture

The system design includes a load-cell module within the overall safety architecture for lifting weight estimation.

  • The module is fully included in the hardware and circuit design.
  • Integration can be finalized after calibration and tuning.
  • This ensures the system remains extensible without limiting the current functional prototype. Together, these modules create a multi-layered safety support system for crane operators and site engineers.

Hardware Components

  • Arduino
  • Ultrasonic distance sensors
  • OV7670 camera module
  • Load Cell sensor
  • Buzzer (audible alarms)
  • Jumper wires & breadboard
  • External power source

Project Structure

  • README.md
  • Codes
  • Diagrams
  • Images

Future Enhancements

  • Full activation and calibration of the weight-monitoring module
  • Integrating all modules into a unified safety dashboard
  • Providing live external view directly to the crane operator
  • Adding wireless communication to send alerts to site supervisors
  • Implementing AI-based object/worker/PPE recognition
  • Enhancing the system using an engineering weight database, enabling AI to analyze whether a crane can safely lift a specific utility load

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