In textile industry, fabric defect relies on human inspection traditionally, which is inaccurate, inconsistent, inefficient and expensive. There were automatic systems developed on the defect detection by identifying the faults in fabric surface using the image and video processing techniques. However, the existing solution has insufficiencies in defect data sharing, backhaul interconnect, maintenance and etc. By evolving to an edge-optimized architecture, we can help textile industry improve fabric quality, reduce operation cost and increase production efficiency. In this session, I’ll share: What’s edge computing and why it’s important to intelligence manufacturing What’s the characteristics, strengths and weaknesses of traditional fabric defect detection method Why textile industry can benefit from edge computing infrastructure How to design and implement an edge-enabled application for fabric defect detection in real-time Insights, synergy and future research directions

1. Shuquan Huang, 99Cloud
Edge Optimized Architecture for Fabric Defect
Detection in Real-time
Vancouver
2018

2. 99Cloud: Build Open Infrastructure For Change
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• Top 3 Contributor in China
• Largest Market Share of OpenStack
in Energy, FSI, etc. in China[1]
• Largest OpenStack Training
Organization in China
• Member of OpenStack, CNCF, Linux,
etc.
[1] “2016 OpenStack Market Analysis”,July 2017, CSDN.net

3. Cloud Dominated the Decades
Cloud computing is:
• the centralization of computing services
• take advantage of a shared data center infrastructure and the
economy of scale to reduce costs.
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SaaS
Software as a
Service
EIM
Incidents
EHS
Tasks
Waste
Use It
PaaS
Platform as a Service
App development
Messaging
Dashboards
Integration
Build with It
IaaS
Infrastructure as a
Service
Networking
Security
System management
Scalability
Move to It

4. 4
A content delivery network (CDN) is a geographically distributed network of proxy servers and their
data centers. The goal is to distribute service spatially relative to end-users to provide high availability
and high performance.
• video streaming, software downloads
• web and mobile content acceleration
• load balancing, analytics and cloud intelligence
Edge Computing Redefines the Cloud
CDN DNS Server
Web DNS Server
Global CDN Load Balancer
Region CDN Load Balancer
CDN Cache Server

5. Edge Computing: Beyond the Data Center
• Highly responsive cloud services -“New applications and microservices”
• Edge analytics in IoT -“Scalable live video analytics”
• Exposure firewall in the IoT -“Crossing the IoT Chasm”
• Mask disruption of cloud services -“Disconnected operation for cloud services”[1]
[1] Mahadev Satyanarayanan, Why Edge Computing is a Disruptive Technology, 2017
Little Optimization for Edge
Computing use cases
Small footprint
Unreliable network
High bandwidth
Low latency
Emerging requirements and
workloads expose cloud’s limitations
Smart cities
Industrial Internet of Things/Industry 4.0
APIs
Data
APP
Cloud
Edge Edge Edge Edge
Gateway Gateway Gateway Gateway
IoT Devices
InternalFactors
ExternalFactors
Augmented Reality
Virtual Reality

6. Industry 1.0 to 4.0: the Evolution of Smart Factories
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7. Edge Computing is the Nerve Ending of Manufacturing
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Consumption
Insight
Cloud
Gateway
Edge
Sensor

8. Architecture Design
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MQTT broker cluster
Edge computing Data source
IoT
device
IoT
device
LAN/LTE/GSM/WiFi
LPWAN(LoRa/SigFox/)
IQRF/ZigBee/Bluetooth/GPIOMQTT
over
Push Intelligence to Edge
Upload data for Cloud analytic
• Rapid application delivery /
update through Containerization.
• Such as:
• To do data analytic for Real-
time decision/action at the
edge.
• Data filtering to reduce latency
to the cloud,
• To secure data transferring.
VPN
• OpenStack cluster/Kubernetes cluster
• Edge server / gateway clustering. For
LB/HA/Scaling
• Containerized for Multi-tenancy
• Containerized for Security
• Application self healing
Visualization
（ELK）
Big data
（Sahara/Spark）
Log Mgt.
（ELK）
Monitoring
（ ELK ）
LBaaS
（Neutron/Kube
Proxy）
MQTT
broker
（Docker）
MQTT
broker
（Docker）
MQTT
broker
（Docker）
Device/gateway Management
OpenStack Cloud platform
SDN
SDS
(Ceph)
Kubernetes master
(Edge managements & Clustering)
Cloud computing
Edge server / intelligent IoT gateway
Data
Analytic
Applications
To interact
With device
Data
Filtering
Data Encrypt
For security
Data
Collector(MQTT-SN)
Edge server / intelligent IoT gateway
Data
Analytic
Applications
To interact
With device
Data
Filtering
Data Encrypt
For security
Data
Collector(MQTT-SN)
Edge server / intelligent IoT gateway
Data
Analytic
Applications
To interact
With device
Data
Filtering
Data Encrypt
For security
Data
Collector(MQTT-SN)
Real-time
action

9. Textile Manufacturing Process
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Spinning
Weaving
Dyeing & Printing
Finishing
Inspection
Delivery

10. Drawbacks of Human Inspection
• Human inspection cannot detect errors due to carelessness, optical illusion and small defects.
• Human inspection fails on detection defects in terms of accuracy, consistency and efficiency.
• Human workers are subject to boredom and thus inaccurate, uncertain inspection results are often
occurred. (100 defects detected per hour)
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11. Traditional Architecture
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Workshop 1 Workshop n
Central Cloud
OT IT
ERP/APS/MES
Data Analytic
Control Plane
Camera/Lens/Light/Printer
High Latency
NO
NO
Data Collection
High Bandwidth

12. Edge Optimized Architecture
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Frame Grabber
Edge
Image Capture
Defect detection
Post-processing
Monitor
Compressed
map of fabric
defects
Inspection Report
（Location, type,
and size of defects）
Camera
#1
Camera
#2
Camera
#N
Fabric Winder
Lighting System
Illumination
Controller
Pixel data
Transport
Encoder
Pixel data
Camera Control
Control
Printer

15. Summary
• Textile quality is traditionally human-oriented analyzed. And few systems are able to operate
weaving and knitting machines.
• With edge optimized architecture, we demonstrate that an automatic and real-time defect detection
system can be built to work in regular texture patterns with high efficiency.
• Auto capture the defect images
• Auto mark the defect position
• Auto count the number of defect and do statistics
• The velocity of the system is 5~6 times the human’s. (120 m/s vs. 20 m/s)
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