Scanflow’s tire sidewall scanning system harnesses mobile edge offline SDK that supports for both Android, iOS, Web and API which accurately and efficiently extract critical information such as DOT, size, model number, and brand from real-time images using smartphones and tablets. This article presents a technically robust account of each pipeline stage, relevant algorithms, and formulaic logic.

System Architecture: Edge-Driven Pipeline

Device & Data Capture Layer

Operators use a mobile app integrated with the Scanflow SDK. Images of the tire sidewall are captured using the built-in camera, under varying environmental conditions (light, dust, wear).

Real-time pre-processing ensures noise reduction and optimal imaging

Iproc = Enhance (Iraw), where Iraw is the input image and Iproc is the denoised, contrast-adjusted output.

Pre-processing workflow for tyre sidewall capture using the Scanflow Core SDK on mobile devices which has Scanflow Customized AI Camera. This step is critical, only high-quality frames make it to later stages, so scanning accuracy starts here.

The app uses Scanflow’s SDK (ScanFlowCameraSession, ScanTrustCameraManager)

• • Requests camera permissions and ensures device orientation (typically portrait).
  • SDK manages autofocus, torch (flash), and zoom, adapting dynamically (for low-light correction, centering prompts, etc.).

Frame Filtering Algorithms:

Every frame is rapidly checked with a sequence of filters:

Sharpness Detection

Uses algorithms like Laplacian variance: computes edge sharpness. If variance is below a threshold, frame is too blurry and discarded.

S=Var(∇2I)

Where S is sharpnes, I frame image.

Motion Blur/Artifact Check

Simple frame-to-frame comparison assesses motion using optical flow or frame difference. If the tyre area shifts too much between frames, it’s rejected.

Exposure and White Balance:

Frames under- or over-exposed (too dark/bright) are detected with pixel intensity statistics:

Only frames passing ALL filters are sent to model inference (segmentation/OCR).

for (Frame frame : cameraBuffer) {
    if (!isSharp(frame)) continue;
    if (isMotionBlurred(frame)) continue;
    if (!hasGoodContrast(frame)) continue;
    if (!isProperlyExposed(frame)) continue;
    if (!isCentered(frame)) continue;
    processFrame(frame); // Pass to segmentation & OCR
}

Image Segmentation & Region of Interest (ROI) Detection

The SDK’s CV engine applies edge detection and region heuristics (Canny, Hough, and deep learning models) to localize key regions:

ROI = (Iproc) ROI = Detect (Iproc)

Segmentation leverages Custome model architectures for instance localization.

Optical Character Recognition (OCR)

• • The cropped sidewall region is processed by a custom OCR model (typically CRNN or CRAFT), tuned on embossed/engraved, low-contrast, and worn characters.
  • Each character in the region outputs a probability vector:

P(ci) = Softmax(zi)

• • • The recognized string S is constructed: S=Concat(argmax(P(ci))

• DOT, TIN, size, and serial/model numbers are extracted using regular expressions and neural attention layers:

DOT = RegexSearch( S, DOT pattern )

Size= RegexSearch( S, Size pattern )

Manufacturer/brand is classified via context signals and dictionary lookups.

Semantic Parsing & Data Structuring

Extracted entities are tagged and validated:

Week/year codes from DOT (e.g., 4-digit decode: YYWW)

Size pattern (Width/Aspect Ratio R Diameter), matched by regex or neural text extraction

Model number filtered by fuzzy match to database records

The feature vector:

Vtire = [DOT, Size, Model_No, Brand]

Local Edge Validation & Timestamping

All critical data is validated on-device using checksum algorithms and cross-checks with reference datasets:

Valid = fcheck( Vtire, DBtire )

Timestamp and geotag are appended for traceability.

Edge Custome Model: Tuning for Tire Sidewall Capture

Model Training & Optimization

Training images are annotated for texture, contrast anomalies, and typical defect cases. Trained with 1 Million data sets

Loss functions combine categorical cross-entropy (for OCR) and segmentation IOU

Ltotal=αLocr+βLiou

Dataset diversity (thousands of brands, types, conditions) ensures generalizability and noise resilience.

The Mobile models are quantized using for real-time, low-latency inference (<300ms typical).

The mobile will completely run on edge with Offline capability for field/yard use.

Data Usage in Model Training

The foundation of Scanflow tire sidewall scanning model lies in meticulously collected, annotated, and curated datasets, incorporating diverse real-world edge cases. The dataset is used for training various AI models that perform segmentation, text detection, and recognition in a multi-stage pipeline:

• Input Data: Raw images and video frames captured from mobile cameras under differing lighting, angles, and tire wear conditions.
• Annotations: Detailed bounding boxes, segmentation masks, and character-level labels enable supervised learning.
• Augmentation: On-the-fly data augmentations such as rotation, scaling, illumination changes, blurring, and noise simulate real-world scanning variations.
• Validation Sets: Separate from training, used continuously across epochs for hyperparameter tuning and generalization checks.

Multi-stage Training

• Stage 1: Backbone Feature Extraction
  • Model: Stabilize and standardized based model architectures.
  • Purpose: Learn low-level and high-level image features common to tire sidewalls.
• Stage 2: Segmentation Training
  • Loss Functions:
    • Classification loss (Lcls) using cross-entropy.
    • Bounding box loss (Lbox) via Smooth L1 or IoU.
    • Mask loss (Lmask) using binary cross-entropy for pixel-wise predictions.

L=Lcls+Lbox+Lmask

Data Privacy and Security For Enterprise System Integration

• Scanflow SDK primarily performs on-device processing, ensuring raw images and processed data never need to leave the mobile device.
• Data export is user-controlled, encrypted, often only metadata or interpreted text is sent to cloud or backend systems.
• Secure key management for SDK licenses maintains system integrity.
  • Local Processing: Scanflow performs all essential OCR and image processing on the mobile device (edge), eliminating the need to send raw images or sensitive data over the network initially.
  • Volatile Memory Storage: Images and intermediate data are kept only in volatile memory buffers during scanning sessions.
  • Immediate Data Purge: Raw capture frames and temporary data buffers are wiped securely immediately after recognition.

Comparison statistics report of Scanflow and other Commercial SDKs available in Market.

Here is a comparative chart that illustrates the stability, accuracy, and performance (speed) of the Scanflow SDK versus three other commercial tire sidewall scanning SDKs. The values are on a 0-100 scale based on typical reported benchmarks and user feedback:

• Scanflow leads across all three parameters with high stability (92), accuracy (95), and performance (90).
• Competitor A follows with decent but lower metrics.
• Competitors B and C lag further behind, especially in accuracy and performance.

This visual comparison helps users quickly comprehend how Scanflow excels in delivering reliable, accurate, and fast tire sidewall scanning.

• Stability indicates how consistently the SDK performs across different tire types, environmental conditions, and mobile devices.
• Accuracy measures the precision of extracted data like DOT codes, size, model numbers.
• Performance refers to inference speed and responsiveness on edge devices (mobile phones).

Users can visualize Scanflow outperforming competitors on all three parameters, indicating reliability and speed combined with superior detection accuracy.

Such a chart helps technical users quickly assess and compare SDK capabilities for integration or evaluation purposes. If needed, this can be presented as a grouped bar chart with distinct colors per metric for clarity.

Let’s take a comparison metrics with leading Competitor A SDK.

Scanflow Leading Metrics (Compared to Competitor A)

Summary

Scanflow’s tire sidewall scanning SDK combines cutting-edge AI models, mobile-optimized processing, comprehensive and accurate data extraction, and seamless integration, backed by industry-leading stability and performance. These technical strengths ensure developers and businesses gain a robust, future proof solution, minimizing operational friction while maximizing insight and efficiency making Scanflow an unmatched choice in the tire scanning ecosystem.

Scanflow delivers enterprise-grade reliability, accuracy, and resilience, positioning itself as the most advanced and deployable tire sidewall scanning SDK in today’s market.

Introduction

Electric vehicle production is reshaping automotive manufacturing in Michigan. Quality expectations are rising, and tires are among the most critical components requiring precise verification. Each tire must match exact specifications, carry correct regulatory codes, and be accurately tracked from warehousing through fitment. Manual checks are slow and error prone, which leads to SKU mismatches, inventory confusion, and assembly delays. This is where tire sidewall scanning powered by Scanflow elevates operations.

Tire sidewall scanning automates the capture of DOT, TIN, tire size, brand, model, and manufacture age codes. This replaces handwritten logs and manual data entry with clean, validated digital information. The result is stronger traceability, improved inventory accuracy, and reliable fitment verification. In this article, we explore how AI driven tire sidewall scanning supports next generation EV production across Michigan, and how Scanflow enables quality, speed, and data integrity for QA, warehousing, and tire fitment teams.

The Need for Tire Sidewall Scanning in EV Production

Why EV production increases tire management complexity

EV manufacturing introduces a higher variety of tire SKUs than conventional vehicles. EV tires must accommodate battery mass, torque delivery, and efficiency requirements. Michigan EV plants often manage dozens of tire sizes, load indexes, and model variants. Manual identification cannot scale reliably across such diversity. Tire sidewall scanning ensures that every tire entering production is verified against the correct attributes.

Risks of manual tire inspection and record keeping

Manual reading of DOT or size markings often results in transcription errors. Operators may misread low contrast characters, overlook model variations, or enter the wrong SKU into the system. These mistakes cause mismatches during fitment and can lead to costly rework. Automating the capture of sidewall fields solves this persistent problem.

The role of accurate tire identification in assembly line quality control

Accurate identification is a fundamental part of EV assembly quality. Validating size, load rating, brand, and manufacturing date ensures each tire meets build requirements. Automated capture from Scanflow provides immediate verification, reduces manual intervention, and strengthens the assembly line’s quality gate.

What Tire Sidewall Scanning Actually Does

Scanflow’s tire sidewall scanning focuses on highly reliable identification and data accuracy. It does not attempt structural defect analysis. Instead, it ensures that every tire is correctly recognized and recorded at every step of production.

High accuracy capture of identification fields

Scanflow reads and validates:

• DOT codes

• TIN codes

• Tire size

• Brand and model text

• Manufacturing date or age code

• Additional molded identifiers when present

This eliminates the inconsistencies of manual reading and supports fast, error free data entry.

Automated data entry and verification

Every captured field is validated against inventory and build data. When a tire is scanned, the system instantly confirms whether it matches the expected SKU or build. This prevents mounting errors, streamlines QA checks, and ensures production alignment.

End to end traceability for EV manufacturing

From warehouse receiving to final assembly, Scanflow creates a complete, searchable data trail for each tire. Time stamps, captured fields, and evidence images support:

• Warranty tracking

• Recall readiness

• Supplier quality evaluations

• Evidence based audits

Traceability becomes effortless because data is collected automatically.

Integration with WMS and MES

Scanflow integrates through APIs to update plant systems in real time. This creates synchronized digital records for warehouse management, production sequencing, and assembly line execution.

Core Capabilities That Matter to QA and Fitment Teams

Reliable reading of molded and printed text

Scanflow’s AI solutions handle the variability of molded tire markings. Differences in depth, curvature, contrast, or wear do not prevent accurate reading.

Support for high throughput operations

The system captures and validates data quickly, allowing warehouse or assembly teams to maintain momentum without waiting for manual logging or double checking.

SKU matching and build compatibility checks

Captured fields are compared against production rules, ensuring:

• Right size

• Right load index

• Right brand and model

• Correct batch or age limitations

This ensures that tires delivered to the line match the vehicle’s exact requirements.

Audit ready, structured data for quality management

Scanflow provides clean datasets for QA teams to analyze patterns, identify process weaknesses, and strengthen quality control strategies.

Benefits for EV Manufacturing Plants in Michigan

Reduced errors and improved fitment accuracy

Automated capture ensures that fitment teams always mount the correct tire on the correct vehicle. Human error is minimized, and quality assurance becomes more predictable.

Faster, more confident operator workflows

Instead of visually checking codes, operators rely on automated validation from Scanflow. This shortens pre-fitment checks and allows lines to maintain speed.

Improved inventory integrity

When intake data is automated, warehouse systems immediately reflect the correct information. Inventory discrepancies decline, and stock becomes more predictable to manage.

Better supplier and batch control

Manufacture age codes and brand model identifiers captured during intake allow QA teams to trace issues back to specific shipments or suppliers. This eliminates guesswork and improves accountability.

Data driven improvement for production teams

Patterns in tire usage, batch age, mismatch frequency, and SKU rotation become visible. This supports continuous improvement, lean initiatives, and operational planning.

How Scanflow Enhances Tire Data Integrity Across the EV Production Chain

Electric vehicle production relies on consistent, verifiable data flows from receiving to assembly. Tire identity must remain accurate throughout, especially when multiple tire SKUs are used for different models. Scanflow ensures that tire data is never lost, misread, or manually corrupted.

1. Improving warehouse accuracy

At receiving, Scanflow confirms the identity of every tire. It creates clean, structured inventory records by capturing:

• DOT

• TIN

• Size

• Manufacturing date

• Brand and model

This prevents mislabeled pallets or mixed batches from entering production. Warehouse teams benefit from reliable, searchable data and simplified rotation planning.

2. Ensuring correct tire pairing during fitment

EV assembly requires perfect matching of tire specification and build requirements. Scanflow validates each tire before it reaches the mounting machine. Operators receive clear pass or mismatch indicators, which protects assembly quality while speeding up decision making.

3. Building traceability at every step

Scanflow assigns digital records throughout receiving, storage, picking, and fitment. Each record includes captured fields, time stamps, and station identifiers. This creates a complete footprint of where each tire was, when it was handled, and how it was used in production.

4. Enabling meaningful analytics

With accurate sidewall data available, plants gain insight into:

• SKU consumption

• Batch aging

• Procurement irregularities

• Warehouse flow patterns

• Fitment verification trends

These insights help QA and operations teams improve both process control and sourcing strategies.

Operational Workflows Supported by Tire Sidewall Scanning

Scanflow fits naturally into existing EV production workflows without requiring major process redesign. It strengthens key steps where manual data entry traditionally introduces errors.

1. Receiving and verification workflow

At intake, operators roll each tire into view of the scanner. Scanflow captures all sidewall fields automatically and updates the inventory system. Incorrect or unexpected tires are flagged immediately, preventing them from entering storage.

2. Inventory management and picking workflow

With digital records available for each tire, WMS processes become more reliable. Staff can quickly locate tires by size, batch, or age. SKU mix ups drop dramatically because data is validated during intake rather than handwritten at the shelf.

3. Pre-fitment validation workflow

As tires reach the assembly station, Scanflow re-verifies sidewall attributes. If the tire does not match the required specification, the system alerts the operator before mounting occurs. This is a strong final safeguard against misfit events.

4. Production documentation workflow

Scanflow records support vehicle build documentation by linking the validated tire attributes to a vehicle ID or VIN in the MES. This improves warranty support, service analysis, and production recordkeeping.

5. Quality, sourcing, and compliance workflow

The data collected supports audits, compliance reporting, supplier quality reviews, and investigations. Because every tire has a digital history, reviews become straightforward rather than paper driven.

Quick Takeaways

• Scanflow captures DOT, TIN, size, brand, model, and manufacture date with high accuracy.

• Automated capture eliminates manual transcription errors.

• Traceability is strengthened from warehouse intake to final assembly.

• Fitment accuracy improves when tire verification is automatic.

• Inventory systems become more reliable with validated real time updates.

• Scanflow provides clean data for continuous improvement and supplier management.

Conclusion

Michigan’s expanding EV production ecosystem depends on precise, reliable, and efficient quality processes. Tires play a central role in both safety and performance, which makes accurate identification and data integrity essential. Scanflow enables plants to automate sidewall data capture, remove manual errors, improve inventory accuracy, and guarantee correct tire fitment.

By starting with warehouse intake and extending through assembly verification, Scanflow provides end to end traceability for every tire. The result is faster workflows, fewer mismatches, clearer supplier insights, and better production documentation. As EV output grows, tire sidewall scanning is becoming a fundamental step toward smarter, more dependable manufacturing operations.