ground crack detection system - emsd

Proposal ID: EMS202002

E&M Inno-Portal Ref. No.: W-0238

Ground Crack Detection System

Prepared by:

Yolanda Au

Business Development Manager

Electrical and Mechanical Services Department

This document is strictly confidential and has been prepared by RaSpect Intelligence Inspection Limited and reviewed by Electrical and Mechanical Services

Department, solely for information use for the introducer of the said Inspection project and may not be taken away, reproduced, redistributed or passed

on, directly or indirectly, to any other person (whether within or outside your organization / firm) or published, in whole or in part, for any purpose.

The information contained in this document has not been independently verified. No representation or warranty expressed or implied is made as to, and

no reliance should be placed on, the fairness, accuracy, completeness or correctness of such information or opinions contained herein. The information

and opinion contained in this document are provided as of the date of this presentation, are subject to change without notice and will not be updated or

otherwise revised to reflect any developments , which may occur after the date of presentation. It is not the intention to provide, and you may not rely on

these materials as providing, a complete or comprehensive analysis of the company’s financial or trading position or prospects, and some of the

information is still in draft form. None of RaSpect Intelligence Inspection Limited or Electrical and Mechanical Services Department, nor any of their

directors, officers, employees, advisers or representatives shall have any liability whatsoever (in negligence or otherwise) for any loss howsoever arising

from any information contained or presented in this document or otherwise arising in connection with this document.

This document contains statements that reflect the company’s current beliefs and expectations about the futures as of the respective dates indicated

herein. These forward- looking statements are based on a number of assumptions , they are not a guarantee of future performance. Accordingly, you

should not place undue reliance on any forward- looking information. Each of the company and the Joint Bookrunners assumes no obligation to update

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The distribution of this document in other jurisdictions may be restricted by law, and persons into whose possession this document comes should inform

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Disclaimer:

2Strictly Private & Confidential

Introduction

Strictly Private & Confidential3

Purpose - Support different level of ground crack detection of a minimum coverage of 15m x 15m

- Classify structural and non-structural crack

- Send out system alert when the monitoring parameters fall below threshold

Executive Summary

Strictly Private & Confidential

B. Monitoring Tools & Software Technology- Professional Grade Visual Camera

- Computer Vision Analytics System- LoRaWAN Data Streaming & Networking System- Realtime Feedback & Alert System

- Report & Data Storage Cloud Platform [https://www.raspect.co]

C. Team- Project Manager

- Software Architect

- Mechanical Engineers

- Artificial Intelligence and Deep learning Experts

A. Key DeliverablesPart 1 – Hardware

- 1A. Define Project Scope

- 1B. Preparation and Installation of Visual Camera

- 1C. LoRaWAN network installation

- 1D. Camera Adjustment and Establish Connectivity

Part 2 – AI Analytics Cloud Platform

- 2A. Visual Data Collection - 2B. Data Analysis through AI Analytics Platform

- 2C. AI Deep-learning Evolution

Part 3 – Continuous Monitoring and Alert System

- 3A. Data Streaming through LoRaWAN- 3B. Periodic Report Generation

- 3C. RaSpect Cloud-based Reporting Platform- 3D. Realtime Alert System

Others - Optional Features

- AI-powered Thermal Analytics

- True-scaled measurable 3D model reconstruction

4

Proposed Solution: Ground Crack Detection System

https://www.raspect.co/


Data Capture

To capture the visual appearance

of the targeted region by visual

camera (Camera model to-be-confirmed)

Data Analytics

To carry out defect identification &

classification, by AI Analytic Engine

Either on Cloud-based, or local

processor

Real time monitoring

To present and display the detection

results, by web portal and integrated dashboard

Data Streaming

To receive the captured image

from the network camera

Reporting & Alert

To notify and alert users when

critical defects are found, through

LoRaWAN communication protocol

AI Evolution Training

To conduct AI deep-learning to

improve the detection accuracy, by

accumulating annotated defects

Executive Summary

System Architecture Flow Chart

Scope of Work

Part 1 – Hardware1A. Define Project Scope

Discuss the site plan with relevant authorities before the commencement of operation, make sure the site environment is

favorable and safe to conduct the hardware installation; For the purpose of accurate result and alert notification, benchmark

for cracks detection and acceptance tolerance will be defined with the authorities.

1B. Preparation and Installation of Visual Camera

Visual Camera (To-be-confirmed) is installed above the targeted/potential ground crack area, ensure the camera is able to

capture the entire ground condition. Extra cameras will be installed when necessary.

1C. LoRaWAN network installation

LoRaWAN network will be installed on site and connection will be established according to Client requirement.

1D. Camera Angle/Position Adjustment and Establish Connectivity

Adjust camera angle or position if necessary, ensure the visual data for entire area of interest is clearly captured. Connect the

camera output to the analysis system and connect with LoRaWAN network .


Scope of Work

Part 2 – AI Analytics Cloud Platform2A. Visual Data Collection

Connect Cameras to control terminal through wireless network, secure power supply for continuous operations. Cameras

capture a set of visual data once a day, or on the authorities’ requirements. The visual data are streamed to the AI

Analytics Engine for real-time data processing.

2B. Data Analysis through AI Analytics Platform

RaSpect AI Analytics Engine process the videos taken simultaneously on site. Identify crack defects through computer

vision technology. AI analytics engine will also analysis the severity of the cracks through AI engine training.


(Ref. Case)

Analysis and Evaluation of Facade Condition

(Ref. Case)

Defects finding by using AI-Powered Analysis Algorithm

Scope of Work


2C. AI Deep-learning Evolution

RaSpect AI Analytics Engine undergoes machine learning during the

operation. With the expansion of data sample, the reliability and

accuracy of the defect detection improve. Evolve into a rapid,

accurate and reliable detection system.

Benefits• Prevent catastrophic damage / accidents

• Diagnose ground cracks at early stage and predict time to failure

• Reduce unnecessary over-haul and maintenance costs

• Realtime monitoring and alert

Possible defects to be identified

• Cracks

• Honeycomb

• Delamination

• Rusting

• Staining

RaSpect Cloud-based Inspection Platform

Part 3 – Continuous Monitoring and Alert System

3A. Data streaming through LoRaWAN

All data assets posted and uploaded to custom-configured RaSpect cloud-based platform.

3B. Periodic Report Generation

A summary report is generated at pre-set intervals, summarizing the inspection findings. Identify cracks development trend,

perform predictive analytics. The materials can be reviewed to a custom-configured, RaSpect cloud-based platform.

3C. Data Visualization

All visual data sorted into folders by regions and timeframe. All recorded data are visualized in PDF catalogue

reference documents.

3D. Realtime Alert System

Prompt alert signal to person-in-charge when general aging and high-risk defects are detected. Real-time alert message

generated to Clients in email / sms or other forms.


Scope of Work

10

Optional Features 1

11

Optional Features 1


(Ref. Case) Automated Thermal Anomalies Detected by

RaSpect AI-powered Façade Analysis Engine

Potential façade defects to be inspected

by visual & thermal:

- Cracks

- Stains

- Rusting

- Spalling

- Corrosion

- Scratch

- Water leakage

- Oxidation

- Coating Damage

Optional Features 1


13

(Ref. Case) Inspection Report summarized potential defect areas that automatically identified by RaSpect AI-powered Façade Analysis Engine

Optional Features 1

True-scaled Measurable 3D model

True-scaled measurable 3D digital model reconstruction of the pre-defined region, 3D model records the entire overview of the

targeted/potential crack area from time to time for defects trends comparison. On-request measurements of specified parameters. Level of

accuracy achieve ± 5cm.


(Ref. Case)

Photorealistic measurable 3D model: (Left) large scale 3D model; (Right) small object 3D model

Optional Features 2

Head Office

+852 59106300

+852 39607100

+86 13028850646

Phone

www.raspect.co

Website

16F, 700 Nathan Road, Kowloon,

HONG KONG.

HKAI Lab

6F, 10, Science Park W Ave,

Science Park, New Territories, HONG KONG.

Contact Us


ground crack detection system - emsd

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