Wenge Intelligent Manufacturing Wenge Intelligent ManufacturingGantry yard vision simulation sample preparation
简体中文English日本語한국어
Submit Similar Need
Home/Cases
AI Vision Sample Delivery Case

Gantry Yard Synthetic Vision Sample Generation Delivery

This case covers a gantry yard and work-vehicle vision demonstration. Based on authorized source materials, the work included scene screening, synthetic sample organization and customer-facing review materials. The delivery contained 48 yard and construction-scene simulation images, 4 classes and 40 AI synthetic samples, 3 foreground pose-variant samples, and Word/PDF review documents. Public content keeps only disclosable delivery facts and does not expose sensitive source information or internal naming. Synthetic samples are used for solution presentation, sample-expansion assessment and discussion; they do not replace real-scene capture, annotation review or formal model acceptance.

48 Scene Simulation Images4 Classes and 40 AI Synthetic Samples3 Pose-Variant Samplesmetadata.csv Sample Records
Preview of AI synthetic samples for gantry vehicle, container, traffic cone and forklift classes

Case Facts

Project type
Synthetic vision sample generation for gantry, yard, construction and work-vehicle recognition demonstrations.
Input scope
Usable scenes and target classes were selected from authorized materials. Public pages do not expose original archive names, temporary paths or internal file sources.
Scene simulation
48 simulation images at 1280 by 720 were prepared across clear day, night yard, rain camera, fog or haze, sunset backlight, motion blur, foreground occlusion and surveillance compression conditions.
AI synthetic samples
The final set contains forklift, crane, container and cone classes, 10 samples per class and 40 samples in total, with metadata.csv recording IDs and source relationships.
Pose variants
Additional finished samples show foreground tilt and orientation changes in city road, construction site and factory backgrounds.
Delivery boundary
The samples support solution preview and sample-expansion assessment. Formal training and release acceptance still require authorization checks, manual annotation review, real-scene validation and metric evaluation.

Project Background

The customer needed to preview synthetic visual samples related to gantry equipment, yard scenes and work vehicles before defining the recognition task, capture scope and model validation route. The work focused on reviewable visual samples and delivery materials, not on promising model performance.

Gantry yard vision demoEarly recognition-task assessmentSample-expansion route confirmation

Material Screening and Scene Organization

The processing stage removed low-relevance ordinary road samples and then generated 48 scene simulation images around port yards, construction sites, warehouse environments and surveillance-camera characteristics. The quality record covered sample counts, scene distribution, brightness range, duplicate candidates and abnormal external-source checks.

Low-relevance sample filtering8 scene conditionsQuality-check record

Four-Class AI Synthetic Samples

Samples were organized into forklift, crane, container and cone classes, with 10 images in each class. The set shows target appearance, pose, background and lighting variation so the business side can discuss annotation rules, capture requirements and sample structure before model training.

Forklift samplesReach-stacker or gantry samplesContainer samplesTraffic-cone samples

Foreground Pose Variants

To explain orientation changes of foreground targets in different backgrounds, finished samples were added for city-road, construction-site and factory scenes. This material is for visual communication and is not a conclusion about real-site distribution or model generalization.

City road backgroundConstruction site backgroundFactory background

Customer Review Materials

The delivery also included Word/PDF review documents and contact-sheet previews, making it easier to inspect sample structure, scene coverage and follow-up capture requirements. Public pages only show screened preview information and do not expose internal source paths or unconfirmed authorization materials.

Word/PDF review documentsContact-sheet previewCapture-requirement discussion

Applicable Scenarios and Boundaries

This delivery pattern fits early solution presentation, sample-expansion assessment, capture-plan discussion and annotation-rule confirmation for vision projects. Training or formal acceptance requires source authorization, duplicate checks, annotation review, train/validation/test splitting, real-site regression and task-metric evaluation.

Early solution presentationCapture-plan discussionFormal validation required later

Need industrial vision sample generation or sample-expansion assessment?

Submit existing materials, target classes, scene scope and model-task goals so the sample boundary, annotation rules and validation route can be confirmed first.

Request Assessment

Case FAQ

These questions clarify how the case content should be used, what can be reused and what materials are useful for assessment.

Does a case equal a formal delivery promise?

No. A case page only describes disclosable project background, technical modules, deliverables and verification boundaries. Actual results, schedule, cost and acceptance criteria depend on devices, samples, site conditions and contract scope.

Can a similar case be reused directly for a new project?

It can be used as a technical reference, but not as a direct reuse commitment. A new project still needs hardware, interfaces, sample authorization, site environment, compliance requirements and acceptance criteria to be checked.

What materials should be prepared for a similar project inquiry?

Useful inputs include target scenario, existing device list, samples or logs, interface protocols, deployment environment, expected deliverables, budget range and acceptance method.

Why are customer site details not shown?

Public case content removes customer names, network addresses, accounts, internal paths, unauthorized materials and sensitive configuration, keeping only disclosable engineering facts and delivery boundaries.

This page is a project case description, not a promise of model accuracy, training effect, delivery schedule or a specific result. Actual plans depend on material authorization, sample scope, annotation rules, model task and contract scope.