01 / The Challenge

Five to eight hours of manual inspection, for every train

No continuous visibility into the locomotive cabin

Three blind spots in day-to-day railway operations

Safety-critical commands fighting railway noise

Railways are one of the world’s largest, oldest, and most safety-critical operating environments. A national network runs 24×7 across thousands of locomotives, tens of thousands of wagons, and millions of voice exchanges on operational radio every year. The standard operating procedures that govern that environment exist because every one of them has historical roots in a near-miss or an actual incident.

And yet what happens to the wagon as it moves through the gantry — the dent on the side panel, the bulge on the roof, the missing door — is reconstructed after the fact, by inspectors working through hours of footage across six camera views per train. The record depends on inspector fatigue, and when a wagon is damaged in transit the dispute between loader, unloader, and rail operator is unresolvable without an evidence-grade comparison of its condition at entry against its condition at exit.

And yet what happens inside the locomotive cabin — mobile use, sleeping, eating or drinking, hand signals not returned, unauthorized persons — is sampled by occasional supervision, not continuously measured. Supervisors cannot ride every cab, investigations begin only once something has already gone wrong, and the long stretches of tunnel and hill section on the network rule out cloud inference for anything safety-critical.

And yet what is said on the operational radio — the caution, the train number, the block clearance — is fighting a constant battle against engine roar, wheel–rail vibration, air-brake hiss, horns, wind, and platform crowd babble. Consumer-grade noise suppression is trained on office and street noise; on a railway it underperforms, and it often suppresses the voice along with the noise.

And yet the condition of every wagon, what happens inside every locomotive cabin, and what is said on every operational radio channel — the three places where railway safety is actually decided — have depended on manual checks, sampled audits and after-the-fact investigation. RailKavach turns each into continuous, evidence-grade observation.

The structural problem is the same in each case: policy without continuous, evidence-grade observation. AiSPRY’s three engagements with the railways were each commissioned to close a different facet of that gap.

LOCATE

No continuous visibility into the cabin

Supervisors can’t ride every cab. Investigations begin only after something goes wrong. Self-reporting depends on the discipline the policy is trying to instill in the first place. Cabin compliance had to be sampled, not measured.

RAILCOMM

Operational radio fighting railway noise

Engine roar, wheel–rail vibration, air-brake hiss, horns, wind, and platform crowd babble compress the signal and erode the listener’s ability to extract critical words — train number, block, signal aspect, caution, stop.

WDD

5–8 hours per train of manual inspection

Manual wagon inspection across six camera views per train depends on inspector fatigue, produces inconsistent records, and is impossible to scale across the volume of freight movements on the national network.

LOCATE

Connectivity-constrained operating environment

Trains spend long stretches in tunnels, hill sections, and low-coverage areas. Cloud inference is not an option for safety-critical real-time monitoring — the system has to run at the edge or not at all.

RAILCOMM

Generic denoisers underperform on railways

Consumer-grade noise suppression is trained on office and street noise. It can’t handle impulsive horns, broadband engine roar, or reverberant tunnels — and it often suppresses voice along with noise. Railway-specific training is non-negotiable.

WDD

No audit trail for accountability disputes

When a wagon is damaged in transit, the dispute between loader, unloader, and rail operator is unresolvable without an evidence-grade comparison of the wagon’s condition at entry vs. exit on a specific journey.

02 / The Solution

Wagon Damage Detection, built for Indian Railways

A six-camera YOLOv11 + PaddleOCR pipeline that inspects freight wagons as the train passes the gantry, reads every wagon number, and produces the entry-vs-exit record that settles a damage dispute — with no one compiling it by hand.

Locate - Locomotive Cabin Video Analytics, built for Indian Railways

An edge-deployed, YOLOv8-based vision model monitors the cabin for the whole journey and flags: using mobile, sleeping, consuming (eating or drinking), hand signal not returned, and unauthorized person. Walkie talkie use, reading and writing, and controlling are recognised as compliant activities and are never counted as violations. It tiers what it finds by severity and routes it to the control room — without depending on network coverage.

Railcomm - Audio Analytics, built for Indian Railways

A railway-trained CNN + RNN + U-Net + Transformer stack that strips engine roar, horns, and platform babble out of operational radio in real time, while protecting the voice and the safety vocabulary carried on it.

One RailKavach suite, three platforms built for Indian Railways

Wagon Damage Detection inspects rolling stock at line speed, Locate watches the locomotive cabin, and Railcomm turns cab-radio audio into classified, searchable transcripts. Choose a platform in the bar above to see it on its own.

Wagon damage maintenance overview: 85.1% of 1,248 wagons damage-free, open defects, damage by category and by severity.
PROJECT 01 · WDD

Wagon Damage Detection — Indian Railways

Six-camera computer vision that inspects freight wagons at line speed and reads every wagon number.

A six-camera YOLOv11 + PaddleOCR computer-vision pipeline that detects eight damage classes on freight wagons across entry and exit gantries, reads wagon numbers via OCR, and produces entry-vs-exit comparison reports — replacing 5–8 hours of manual review per train with roughly 2 hours of fully automated processing.

2 hrs per train99.2% frame detection98% wagon-number OCR8 damage classes
Watch the WDD demo View project
Locate compliance dashboard, Railway Board view: 91% compliance, 708 trips analysed, 248 confirmed violations, and violation types across the network.
PROJECT 02 · LOCATE

Locate - Locomotive Cabin Video Analytics

YOLOv8-based detection of cab violations from CVVRS footage, with division-scoped access.

YOLOv8-based detection of cab violations from Cab Video & Voice Recording System (CVVRS) footage, with division-scoped access. It flags using mobile, sleeping, consuming (eating or drinking), hand signal not returned, and unauthorized person; walkie talkie use, reading and writing, and controlling are recognised as compliant and never counted — with severity-tiered alerts and tamper-evident evidence clips.

5 violation types100% journey coverage3 compliant activities24×7 monitoring
Watch the Locate - Locomotive Cabin Video Analytics demo View project
Railcomm transcripts: cab radio lines in Hindi and English, each classified, with a safety-device bypass and an over-speed flagged.
PROJECT 03 · RAILCOMM

Railcomm - Audio Analytics

Railway-trained audio AI that strips noise out of cab radio and flags safety call-outs in every transcript.

A deep-learning audio enhancement platform — CNN + RNN + U-Net + Transformer — trained on real railway noise (engine, wheel–rail, horns, wind, tunnel reverb, crowd) that filters background in real time while preserving voice formants and boosting safety-critical vocabulary on every operational channel.

85% noise reduction95% communication clarity60% fewer miscommunicationsFail-open never silences a channel
Watch the Railcomm - Audio Analytics demo View project
YOLOv11PaddleOCRYOLOv8PyTorchOpenCVCNN + RNNU-Net AudioTransformer VoiceFastAPIVite + ReactCLAHEPostgreSQLMongoDBCelery + RedisEdge Computing
Demonstration

Wagon Damage Detection, recorded in production

Locate - Locomotive Cabin Video Analytics, recorded in production

Railcomm - Audio Analytics, recorded in production

RailKavach, recorded in production

The recording below is the actual operator interface running on live railway data — not a mock-up — shown beside the audited numbers this platform is held to.

Demo 01

Wagon Damage Detection

Six-camera YOLOv11 + PaddleOCR inspection running as the train passes — eight damage classes detected, wagon numbers read via OCR, and entry-vs-exit comparison reports generated with no human in the loop.

2 hrsper train, from 5–899.2%frame detection98%wagon-number OCR8damage classes
TrainVision — comparison entries5:01
Demo 02

Locate - Locomotive Cabin Video Analytics

Edge-deployed YOLOv8-based vision continuously watching the locomotive cabin — compliance violations detected and severity-tiered alerts raised, with tamper-evident evidence clips.

5violation types100%journey coverage3compliant activities24×7monitoring
Locate — upload, inspection, verification and dashboards0:53
Demo 03

Railcomm - Audio Analytics

A CNN + RNN + U-Net + Transformer pipeline filtering real railway noise from operational radio in real time — while preserving the safety-critical voice content on every channel.

85%noise reduction95%communication clarity60%fewer miscommunicationsFail-opennever silences a channel
Operations & Audio Analysis — safety compliance0:58
03 / Architecture

A shared five-stage operating model across the three platforms

From sensor capture through railway-specific deep learning to evidence-grade outputs — the three platforms differ in modality (vision in Locate - Locomotive Cabin Video Analytics and WDD, audio in Railcomm - Audio Analytics) but share the same architectural rhythm: capture, infer at the edge or in real time, attribute events to context, surface to operators, and write to a tamper-evident archive.

01 · CAPTURE
Sensors on the Asset
Cabin cameras + mics (Locate) · gantry cameras (WDD) · radio & intercom channels (Railcomm). Embedded timestamps for downstream alignment.
02 · INFER
Railway-Trained Models
YOLOv8 vision pipeline for cab activity detection (Locate) · YOLOv11 wagon inspection (WDD) · CNN + RNN + U-Net + Transformer audio stack (Railcomm). Trained on annotated railway data, not generic surveillance footage or office noise.
03 · ATTRIBUTE
Context & Comparison
Entry-vs-exit damage attribution (WDD) · severity tiers + duplicate suppression (Locate) · channel quality scoring + critical-word boost (Railcomm).
04 · SURFACE
Operators & Dashboards
Control-room alerts, safety officer notifications, TrainVision wagon dashboard, channel-health views — the human-in-the-loop layer.
05 · EVIDENCE
Tamper-Evident Archive
Encrypted in transit, role-based, division-scoped access, cryptographic chain of custody. Outputs feed incident review, SOP enforcement, training, and accountability decisions.
04 / Results

What Wagon Damage Detection delivers in production

What Locate - Locomotive Cabin Video Analytics delivers in production

What Railcomm - Audio Analytics delivers in production

What RailKavach delivers in production

Every number below is measured in operation against the target this platform was engineered to — and beyond the numbers, it moves that part of railway operations from episodic and reactive to continuous and evidence-backed.

WDD · WAGON INSPECTION

Inspection collapsed — with no human in the loop

  • 2 hours per train — down from 5–8 hours of manual review
  • 80–90% reduction in manual review time
  • 99.2% frame detection rate end-to-end
  • 98% wagon-number OCR in the dashboard
  • Zero manual intervention — pipeline runs without an operator
  • 100% cross-view consistency — left, right, top reconciled
LOCATE · CABIN VIDEO ANALYTICS

Continuous, evidence-backed cabin safety

  • 100% journey coverage — every minute monitored
  • Five SOP violations detected; walkie talkie, reading and writing, and controlling recognised as compliant
  • Every detection confirmed or rejected by a reviewer, with who and when recorded
  • Severity-tiered alert routing to control rooms
  • Tamper-evident clips with geo + time stamps
  • Locopilot scorecards drive training, not just enforcement
RAILCOMM · AUDIO ANALYTICS

Clean radio for safety-critical commands

  • 85% background noise reduction on operational channels
  • 95% communication clarity for locopilot–stationmaster exchanges
  • 60% fewer miscommunications in day-to-day operations
  • Safety vocabulary (caution, stop, signal, block, train numbers) preserved and boosted
  • Fail-open design — AI never silences a safety channel
  • Tamper-evident audit trail across all critical channels
05 / Frequently Asked

Questions about the three railway platforms

What three projects has AiSPRY delivered for Indian Railways?
Three production AI platforms. Locate - Locomotive Cabin Video Analytics — a YOLOv8-based platform that detects cab violations from locomotive video, with 100% journey coverage. CVVRS is the Cab Video & Voice Recording System fitted in Indian Railways locomotives; Locate analyses the footage these recorders already capture. The Railcomm - Audio Analytics — a deep-learning audio enhancement platform (CNN + RNN + U-Net + Transformer) that reduces background noise on operational radio channels by 85%, raises communication clarity to 95%, and cuts miscommunications by 60%. And the Wagon Damage Detection (WDD) platform — a six-camera YOLOv11 + PaddleOCR pipeline that automates freight wagon inspection, taking inspection time from 5–8 hours per train down to roughly 2 hours with a 99.2% frame detection rate.
Why do all three platforms run AI at the edge or in real time?
Railways are real-time, safety-critical operations. Trains routinely pass through tunnels, hill sections, and low-coverage zones where cloud inference is not an option, so Locate - Locomotive Cabin Video Analytics runs locomotive-edge inference for 100% journey coverage. Operational radio is a live conversation, so the Railcomm - Audio Analytics runs streaming inference with latency budgets engineered to keep conversations natural. And wagons pass through a gantry once — there is no second chance to inspect them — so the WDD pipeline runs detection as the train moves.
How do the three platforms reinforce each other?
They form a unified observability layer across the railway operating environment. Locate - Locomotive Cabin Video Analytics (CVVRS footage analytics) makes what happens inside the cabin visible. Railcomm - Audio Analytics makes what is said on the radio audible. WDD makes what happens to the rolling stock evidence-grade. Together they convert episodic, reactive railway operations into a continuous, evidence-backed, AI-augmented operating practice — with every minute of every journey, every safety-critical command, and every wagon-handling event observable and auditable. The Locate - Locomotive Cabin Video Analytics voice channel can be enriched by Railcomm - Audio Analytics’s clean audio; the wagon-handling events from WDD can be cross-referenced with cabin behaviour from Locate - Locomotive Cabin Video Analytics in an incident review.
What does Locate detect?
Using mobile, Sleeping, Consuming (eating or drinking), Hand signal not returned, and Unauthorized person. Walkie talkie use, reading and writing, and controlling are recognised as compliant and are not counted.
In Locate, who can see what?
Access is by role (Super Admin, Division Admin, Reviewer, Viewer) and scoped by division. Each division sees only its own crews, trips and detections. Zonal and headquarters users can be given access across divisions. Every confirm or reject is recorded with who made it and when. All data is encrypted in transit.
Why train railway-specific models rather than using off-the-shelf computer vision and noise suppression?
Off-the-shelf models are trained on the wrong data. Generic surveillance models miss the imaging realities of a cabin (low light, vibration, sun glare, occlusion) and a gantry (fast-moving wagons, varied lighting, missed frames). Consumer denoisers are trained on office and street noise — they underperform on impulsive horns, broadband engine roar, and reverberant tunnels, and often suppress voice along with noise. All three AiSPRY platforms are trained on real Indian Railways data, so the models recognize their operating environment and protect what matters — voice formants, safety-critical vocabulary, wagon-vs-engine distinction.
Are these systems intrusive or surveillance-oriented?
No. All three platforms are engineered as safety systems, not surveillance systems. Locate - Locomotive Cabin Video Analytics monitors compliance against rules that already exist in railway SOPs; severity tiers, duplicate suppression, and low false-positive design keep the alert stream operational rather than punitive, and outputs feed coaching and training as well as enforcement. Railcomm - Audio Analytics preserves voice clarity, protects safety-critical vocabulary, and fails open so that AI never silences a safety channel. WDD is engineered against accountability disputes between loaders, unloaders, and the rail operator — its outputs settle facts, they don’t replace judgment. All three are protected by encryption in transit and role-based access control.
What measurable outcomes have these platforms delivered?
Locate - Locomotive Cabin Video Analytics — 100% journey coverage, with every detection confirmed or rejected by a reviewer. Railcomm - Audio Analytics — 85% background noise reduction, 95% communication clarity, 60% fewer miscommunications. WDD — inspection time down from 5–8 hours to 2 hours per train, 80–90% manual review reduction, 99.2% frame detection rate, 98% wagon-number OCR accuracy, zero manual intervention end-to-end, 100% cross-view consistency.
— Build a flagship AI platform with AiSPRY

Safety-critical operating environments where AI converts real-time signals into trustworthy decisions.

From cabin compliance and audio denoising to wagon damage detection — AiSPRY designs and ships the AI platforms that ground continuous observation in real workflows, real SOPs, and real evidence.