AI Visual Inspection Equipment for Footwear Materials
Footwear material AI visual inspection equipment: for footwear components such as flyknit uppers, mesh uppers, shoe quarters, tongues, insoles and EVA midsoles, it inspects yarn breakage, fuzz, color differences, damage, flash and printed logo defects, and outputs OK/NG.
Products Overview
Flyknit uppers have complex textures, making them the most difficult category in footwear material inspection
Footwear material AI visual inspection equipment is used to inspect flyknit uppers, mesh fabric uppers, quarters, tongues, insoles, and EVA midsoles. With industrial cameras and custom lighting it identifies yarn breakage, fuzz, color differences, damage, flash, and printed mark defects, outputs OK/NG, and interlocks with the production line.
The inspection difficulty for footwear materials centers on Upper: Flyknit uppers are formed by knitted yarn, and the texture itself is a complex pattern, so defects such as yarn breakage, dropped stitches and yarn misalignment have very low contrast against the patterned background.
By comparison, inspection of EVA midsoles and soles is closer to routine injection molded parts: defects such as flash, short shot, color difference, and indentation have relatively clear forms.
Therefore footwear material inspection is usually designed part by part: uppers focus on fine weaving defects, while soles and midsoles focus on injection molding and foaming defects.
- Covered components: flyknit uppers, mesh uppers, shoe quarters, tongues, insoles, and EVA midsoles
- Challenging parts: yarn breakage and dropped stitches in flyknit uppers
- Common Components: Injection Molding and Foaming Defects in EVA Midsoles and Outsoles
- Solution strategy: inspection items and algorithms configured separately by component
Core Functions
What the Equipment Can Do and How Far It Can Go
Flyknit Upper Inspection
For woven structures, it identifies yarn breakage, dropped stitches, yarn misalignment, and uneven weaving.
Mesh Fabric Inspection
Inspection of holes, weaving defects, and color difference in mesh fabric.
Sole and Midsole Inspection
Flash, short shot, color difference, and indentation on EVA foam parts.
Print Mark and Decoration Inspection
Position, clarity and completeness of the Logo and printed marks.
Outline Contour Measurement
Dimension check of upper shape and insole contour.
Per-component Configuration
Different components use different light sources and algorithm recipes.
inspection Object
Shoe Uppers
Flyknit, mesh fabric and knitted uppers, with the focus on weaving defects
Midsole and Outsole
EVA and TPU foam parts, with the focus on injection molding and foaming defects
Lining and Insole
Appearance and contour inspection of insoles and linings
Decoration and Printed Marking
Quality check of logos, printed marks, and heat-pressed decorations
inspection defect
| Defect Types | Typical Components | Inspection Focus Points |
|---|---|---|
| Yarn Breakage / Dropped Stitch | Flyknit Upper | Low contrast against patterned backgrounds, requiring dedicated illumination |
| Fuzz / Floating Yarn | Shoe Uppers, Mesh Fabric | Distinguished from normal fuzz |
| color difference | Shoe Uppers, Soles | Requires a stable light source datum |
| Damage / Hole | Mesh Fabric, Shoe Uppers | Transmitted-light imaging is more direct |
| Flash / Burr | EVA midsole | Contour edge extraction |
| Short Shot / Sink Mark | EVA midsole | Regular shape; contour and grayscale can be combined |
| Printed Marking Defects | Shoe Uppers, Insoles | Position, clarity and completeness |
Working Principles
- 01 Footwear component loading
- 02 Positioning and Fixing
- 03 Dedicated illumination imaging
- 04 Weaving defect inspection
- 05 Injection molding and foaming defect inspection
- 06 Print mark and contour inspection
- 07 Result Composition OK / NG
- 08 NG rejection
- 09 Data Recording
Relationship Between Imaging and Judgement
The key to flyknit upper inspection is Separating "Pattern" from "Defect". Because the upper pattern itself is a complex gray-level variation, an absolute threshold will definitely produce many false calls. A workable path is to let the algorithm learn the normal distribution characteristics of the pattern and judge significant deviations as defects.
Vision System
How Cameras, Lenses, Light Sources and Controllers Are Configured
industrial camera
Select area-scan or line-scan by field of view and minimum defect size; continuous web materials usually use line-scan cameras
- Area-scan camera: fixed-shot, single-piece and intermittent feed applications
- Line-scan camera: for continuous material travel, wide areas, and even-interval imaging
- Pixel equivalent is derived backwards from "minimum resolvable defect ÷ desired pixel count", rather than fixing the camera first
lens
Determines field of view, distortion, and depth of field; precision measurement requires telecentric lenses
- Standard industrial lens: low cost, suitable for appearance inspection
- Low-distortion lens: for large-format applications where the edges must also be judged
- Telecentric lens: suitable for hole diameter, contour and dimensional measurement
- Depth of field must match material waviness and fixture repeat positioning accuracy
Light Source and Illumination
Whether defects in flexible materials can be captured depends largely on the lighting
- Diffuse light: uniform illumination, suitable for color difference and stain applications
- Low-angle light: highlights scratches, indentations, wrinkles and other surface relief
- Backlight/transmitted light: highlights holes, damage and short shot
- Coaxial light / stripe light: suppresses reflection, suitable for coated or highly reflective surfaces
Controllers and Industrial PCs
The platform for running algorithms, outputting results and interlocking with the production line
- An industrial computer or vision controller runs the algorithms
- A light source controller handles brightness adjustment and strobe synchronization
- Interacts with the PLC to complete interlocking, alarming, and rejection
AI algorithm
Choose the Algorithm by Defect Form, Not by Complexity
Template Matching and Positioning
Locate first, then judge. Every defect judgment is built on a stable coordinate system.
- Shape matching / gray-scale matching: good stability, suitable for fixed stations
- Align first, then split the regions, to avoid false calls caused by position drift
Blob and Morphological Analysis
Suited to defects such as stains, holes and foreign matter that show a clear grayscale/color difference from the background
- Threshold segmentation → connected component statistics → judgement by area / aspect ratio / circularity
- High requirements on illumination stability; the light source must work with the fixture
Edge and Contour Measurement
Suitable for dimension, contour, hole position and spacing judgements
- Sub-pixel edge extraction to obtain a contour point set
- Fit lines/circles/arcs and calculate length, diameter, angle and position tolerance
Deep Learning (Classification / Detection / Segmentation)
Suited to defects such as scratches, wrinkles and skipped stitches, whose shapes vary and are hard to describe with rules
- When defect forms are irregular, it is difficult for rule-based algorithms to enumerate them all
- Requires OK / NG sample training; sample quantity and coverage determine the upper limit
- Enables pixel-level segmentation and outputs defect length, width, area and position
Automatic Alarm and rejection
How inspection results act on the production line
Inspection results are not only shown on a screen. Workpieces judged NG need to be Mark the position and interlock rejection or sorting, and archives the image and judgement result for that part for later traceability and re-judgement.
The alarm method is set according to site practice: audible and visual alarm, on-screen pop-up, PLC set bit, or all three at once. Critical defects and general defects can use different handling strategies — the former stops the machine and alarms, the latter is only marked.
- 01 image acquisition
- 02 Positioning and region segmentation
- 03 Defect Judgement
- 04 Combine results into a single OK / NG per piece
- 05 Result sent to PLC
- 06 NG Rejection / Sorting
- 07 Image and data archiving
data traceability
Records, Queries and Quality Closed Loop
Per-Piece Records
The judgment result, defect type, defect position and timestamp of every part are written to the database
- Supports retrieval by time, batch and defect type
- NG image retention for re-judgement
Batch and Recipe
Different products use different recipes, called up at changeover to reduce manual parameter tuning
- Recipes store the inspection region, thresholds and algorithm parameters
Production Line Data Integration
Exchange inspection data with the MES / host system
- Output pass rate, defect distribution and other statistics
- The interface method depends on the site system
equipment configuration
Optional Configuration Items and Selection Logic
| Configuration Item | Description |
|---|---|
| Applicable Components | Flyknit Uppers / Mesh Fabric Uppers / Shoe Quarters / Tongues / Insoles / EVA Midsoles |
| Inspection Method | Dedicated lighting + industrial camera + AI algorithm |
| Changeover Method | Recipe Switching |
| Inspection Speed | To be added |
| inspection accuracy | To be added |
| communication method | I/O · TCP · RS485 · Modbus · S7 · Profinet |
| Enclosure and Power Supply | To Be Confirmed on Site |
Application Industry
Which Industry This Equipment Is Usually Installed In
Applicable Materials
Detectable Material Types
Common Question
Can yarn breakage in flyknit uppers be detected reliably?
Is EVA Midsole Inspection Difficult?
Can Uppers and Soles Be Inspected on One Machine?
What If Footwear Material Colors and Styles Change Frequently?
Can the inspection cycle time keep up with the production line?
What Information Do You Need to Provide?
Submit sample testing
Inspection of flyknit uppers requires physical validation. Please provide samples of different styles and defects, and we will first run small-batch imaging and judgement tests.
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Send Us Your Workpiece and We Will Show You the Measured Results
Equipment configuration varies with the inspection object, field of view and cycle time. Provide OK and NG samples and we will run actual imaging and judgement tests and recommend the corresponding model and configuration.