Overview

Labeling and signage are critical elements of railway infrastructure, supporting both operational efficiency and public safety. Whether it's directional signage, milestone labels, or tactile markers for the visually impaired, any misplacement, damage, or omission can cause confusion, safety hazards, or regulatory non-compliance.

NorrStudio, developed by NorrSpect, brings artificial intelligence to this often-overlooked area—ensuring consistent inspection, defect detection, and compliance verification for all critical identification and labeling components in rail networks.

About NorrSpect

Based in Umeå, Sweden, NorrSpect is a leader in AI-based inspection systems. With a legacy of solving complex quality challenges for top-tier manufacturers like Volvo Cars, NorrSpect brings deep expertise in visual AI and operational automation to the railway and infrastructure sectors, helping public and private rail networks achieve higher levels of quality assurance and safety compliance.

The Challenge: Overlooked, Yet Mission-Critical Defects

Labeling and identification elements are often manually checked—if at all. Yet small oversights like a missing directional sign or a tilted name board can result in misrouted passengers, delayed operations, or failure to meet regulatory requirements (especially for accessibility and safety).

Key pain points include:

• Weathered or sun-faded signs going unnoticed

• Installation misalignment impacting readability

• Misplaced safety or emergency decals

• Incorrect labeling of tracks or milestones

• Non-compliant tactile elements for accessibility

Common Defects Detected by NorrStudio

• Worn or illegible track number signage

• Tilted or partially obscured station name boards

• Faded safety decals on gates, fences, or platforms

• Wrong milestone or track segment labels

• Directional signage pointing in the incorrect direction

• Misaligned tactile paving affecting accessibility for visually impaired passengers

NorrStudio in Action: Labeling Assurance with AI Precision

Using advanced AI models trained on railway-specific data, NorrStudio inspects signs, labels, tactile features, and wayfinding aids with high-resolution imagery and pattern analysis. The platform identifies wear, misplacement, visibility issues, and regulatory gaps—flagging them for correction before they impact operations or compliance.

Key Capabilities:

• Text legibility analysis for worn or faded signage

• Angle and alignment checks for mounted boards or labels

• Color and contrast detection to assess visual compliance in varied lighting

• Symbol and icon verification for safety and wayfinding signs

• Surface mapping of tactile paving to ensure correct placement and orientation

• GPS + vision integration to match signs with correct locations

Deployment Snapshot: Ensuring Safe and Clear Labeling in Metro Stations

Problem:

A regional metro system received complaints about unclear signage and misaligned tactile paths, creating navigation difficulties for passengers—especially during peak hours and for those with disabilities.

Solution:

NorrStudio was installed in mobile and fixed-position configurations across multiple stations. The system scanned all visible signage and tactile paths during off-peak hours, comparing their position, clarity, and orientation against the digital station layout.

Result:

• Identified and corrected 37 mislabeled directional signs across 12 stations

• Detected 16 tactile paving misalignments posing accessibility issues

• Flagged 22 signs for replacement due to faded text or incorrect font sizes

• Enabled maintenance teams to prioritize signage updates based on risk level

Measurable Impact

Why Rail Operators Trust NorrStudio

• Reduces safety and compliance risks from mislabeled or damaged signage

• Provides consistent, data-driven inspections across all infrastructure points

• Tailors models for regional standards and languages

• Supports integration with GIS, BIM, and asset management tools

• Enables smart maintenance planning through predictive labeling wear analysis