The system would be able to autonomously identify and analyse the facade elements and defects, and classify the anomalies according to the type, confidence level and severity or magnitude
Singapore’s Building and Construction Authority (BCA) and
Housing & Development Board (HDB) have launched
a Call for Proposal on using drones for Building Facade Inspection. This is supported
by the Partnerships for Capability Transformation through Government Lead
Demand initiative (Gov-PACT).
The call is open to all local SMEs/startups, Institutes of Higher
Learning (IHL) & Research Institutes (RI).
(Under Gov-PACT, the
Government serves as the large organisation with which SMEs/startups will work
with to develop and test-bed innovations. S$80 million was allocated for Gov-Pact
under SPRING’s Partnership (PACT) programme as part of MTI-SPRING’s initiatives
announced at the Committee of Supply (COS) 2017.)
The objective of this Call for Proposal is to develop an
integrated inspection system, using advanced image-capturing drone and
computing technologies, assisted by artificial intelligence (AI) to perform
autonomous defect detection, based on image recognition and machine learning.
This is expected to aid the automation of building façade inspection, improve
workflow, promote collaboration between inspection parties as well as improving
the process of reviewing and assessing the condition of building facade.
Building facades need to be regularly inspected and
maintained to ensure that the façade elements (e.g. wall plasters, tiles and
claddings) or any installations (e.g. awnings, sunshades and air-conditioner
units) that attach to the facade will remain intact so that they do not fall
and pose a danger to the people and properties below them. However, inspection
of facades can be a challenging task and the problem is further aggravated by
the increasing building height and complexity of the facade element.
Currently, inspecting building facades is a slow, resource
and manpower intensive process. It usually involves a long, tedious and costly
process that requires the inspector to work at height using scaffold, gondola,
boom lift or even rope access. There are concerns over personnel safety. Moreover,
the reliability of traditional inspection method relies heavily on experience
and judgement of the inspector. Such inspection may also be delegated to
lowerskilled workers who may not be equipped with the competency required for
BCA and HDB feel that there is an urgent need of a new
inspection model, using innovative solutions to improve the inspection
efficiency, reduce safety risk to personnel, save time and at the same time
being cost effective.
The scope of inspection under this Call for Proposal covers
all types of building façades, exterior features, fixtures and appendages to
the external walls which shall include, but not be limited to, plaster,
concrete, tiles, siding, bricks, cornices; curtain walls; claddings; awnings
and sun-shading devices and other architectural features and projections. Inspection priority shall be given to plaster,
concrete, tiles and any exterior projections from the building footprint.
Condition of the facade in this context refers to its
safety, stability and integrity. Quality and functionality of the facade i.e.
energy loss and air leakage are beyond the scope of inspection.
The project can be categorized into 2 main phases – Phase 1
comprising of Phase 1A & 1B, and Phase 2 comprising of Phase 2A & 2B.
Image mapping and
processing software developed concurrently with drone field trials
According to the Problem Statement document,
Phase 1A consists of the development of drone image mapping & processing
software. The software should provide mapping and image indexing for traceability
(position, geolocation, date, time of survey, etc.).
Images should be organised to allow easy browsing and
navigation, identify and mark the defects or anomalies, make annotation and
present the results for reporting purpose. It is envisaged that the image
databank would contain images and of various façade elements and defects
commonly found in building facade. Daylight visual and thermal images should be
integrated to be examined in tandem and providing side-by-side comparison.
The solution should also enable Visual 3D reconstruction of
building for general building visualization and localisation of defects with
the ability to pan, rotate and zoom. The 3D rendered model should be linked to
an individual or group of high resolution images to allow closer examination by
inspectors. The model should allow overlaying of defects or anomalies on its
The program’s basic functions such as viewing and browsing
of images, marking of defects and anomalies, and making annotations should be
made accessible via mobile devices using apps.
The programs developed shall apply AI to perform autonomous
defect detection, based on image recognition and machine learning. The proposed
artificial intelligence module, together with the image databank of the
defects, are to be developed as an extension (add-ons/plugins) to the program.
The aim is to design a program which could autonomously identify
and analyse the facade elements and defects, determine and classify the
anomalies according to the type, confidence level and measurement of the severity or
magnitude. For Phase 1A, the defects required for auto-recognition should
minimally include a) cracks, b) spalling (include early indication and
tell-tale sign leading to spalling) on concrete, plaster and tile surfaces.
To ensure the accuracy of the defects detection, Inspector
shall conduct close-up inspection to validate defect images collected during
the field trials (under Phase 1B). The image recognition system shall be
updated and retrained to improve the detection accuracy.
Phase 1B involves drone field trials to be conducted on 2
existing buildings, assigned by BCA / HDB, on 2 separate occasions. This will
conducted concurrently with Phase 1A. Drones are to be deployed to perform
survey on the condition on the entire façade areas of the building using both
visible light and infrared thermal cameras (other alternative scanning
technology can be proposed).
During Phase 2A, the autonomous defect recognition scope and capability
of the program developed in Phase 1 will be extended to things such as void/
hollow/ delamination; efflorescence/discolouration on concrete, plaster and
tile surface; and metallic corrosion. Additionally, in this phase, the defects have to be assessed, by measuring the
magnitude and/or analysing the patterns and they have to be classified according
to the severity of the defects.
In addition to visual imaging, Phase
2A should include the image recognition and analysis using thermal infrared
imaging (or the adopted alternative scanning technique) in providing a more
accurate detection of the defects. Applicants will conduct one field trial on
an existing building to assist in developing the program and defect recognition
capability. The defects should be verified by Inspector with a close-up site
inspection and results further retrained.
Finally, in Phase 2B, one existing building will be assigned
by BCA / HDB to demonstrate the effectiveness of the solution through a pilot
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