Object Detection Datasets for Computer Vision AI
High-precision bounding box and polygon datasets across custom object taxonomies, environments, and sensor types. Built for production object detection models that generalize beyond public benchmarks.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Public Detection Benchmarks
COCO, Pascal VOC, and Open Images advanced object detection research. Production detection systems require domain-specific object classes, operating environment diversity, and rare object coverage those benchmarks cannot provide.
Industrial inspection, retail shelf monitoring, and autonomous systems detect objects outside the 80-1,000 classes in public datasets. Models trained on COCO fail on custom industrial parts, proprietary products, and environment-specific objects.
Off-the-shelf object detection datasets suffer from class taxonomy gaps for domain-specific objects and environmental mismatch between benchmark imagery and your sensor, lighting, and deployment conditions.
LXT builds custom object detection datasets with your class taxonomy, sensor configuration, and environmental coverage. We deliver bounding box and segmentation annotations at the precision and scale your production detection system requires.
Why Teams Upgrade
Limitations of Public Object Detection Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| COCO | 80 common object classes; consumer photography bias; no domain-specific industrial, medical, or specialized object types | 80 classes only |
| Pascal VOC | 20 classes; aging benchmark with limited scene diversity; no small object or dense packing coverage | 20 classes only |
| Open Images V7 | 600 classes but shallow per-class instance counts; inconsistent annotation quality across contributor batches | Shallow counts |
| Objects365 | 365 classes with web image bias; limited industrial, overhead, or specialized sensor coverage | Web bias |
| LVIS | 1,200 long-tail classes but very few instances per rare class; insufficient rare object training data for production | Rare-class sparse |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Object Classes
Taxonomy Configuration
- Custom Classes: Domain-specific object taxonomy designed for your detection use case
- Hierarchy: Parent and child class relationships for hierarchical detection models
- Negative Examples: Hard negatives and confusable class pairs for decision boundary training
Annotation Types
Label Formats
- Bounding Boxes: Axis-aligned and rotated bounding boxes per object instance
- Polygons: Instance polygon annotations for irregular object shapes
- Keypoints: Object keypoint annotations for orientation and pose-aware detection
Collection Scope
Environmental Coverage
- Sensor Types: RGB, IR, thermal, fisheye, and domain-specific sensor configurations
- Lighting: Daylight, dusk, night, artificial, and mixed lighting conditions
- Environments: Indoor, outdoor, aerial, underwater, and specialized deployment contexts
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in Object Detection
High-accuracy models handle rare attributes that public datasets miss.
Small and Densely Packed Objects
Retail shelves, aerial scenes, and crowd images contain dozens of small, overlapping objects. Precise small-object annotation with minimum pixel dimension filters ensures training quality.
Partial Occlusion and Truncation
Real-world objects are frequently partially hidden or cut off at image edges. Explicit occlusion and truncation flags with visibility estimates support robust detection training.
Rare and Long-Tail Object Instances
Infrequent but important objects need minimum instance count guarantees. Targeted collection campaigns and augmentation documentation ensure rare classes meet training thresholds.
Domain-Specific Object Variation
Industrial parts, retail products, and specialized objects appear in highly variable conditions. Domain-specific annotation guidelines capture inter-class confusion boundaries for your detection taxonomy.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Bounding Box and Polygon Annotation
Expert annotators draw tight bounding boxes and polygons per object instance. Multi-pass quality review ensures annotation precision above 95% IoU for production standards.
Instance Segmentation
Pixel-level instance masks for each detected object, supporting both detection and segmentation model architectures from a single annotation pass.
Attribute and Metadata Labeling
Per-instance attribute labels (color, material, state, orientation) and occlusion, truncation, and crowd flags for fine-grained detection model training.
Industry Applications
Object Detection Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Industrial Inspection
Defect detection, part recognition, assembly verification
Retail Analytics
Shelf monitoring, product detection, planogram compliance
Autonomous Driving
Multi-class road object detection, ADAS
Drone and UAV
Aerial object detection, infrastructure monitoring
Medical Imaging
Lesion detection, anatomical landmark localization
Security Systems
Person detection, threat object screening
Agriculture
Crop disease, pest, and livestock detection
Robotics
Object recognition for manipulation and navigation
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Object Detection Dataset FAQs
Can you annotate domain-specific objects not in public datasets?+
Yes. We build custom annotation guidelines for any object class. We start with a taxonomy review and sample annotation calibration before full production annotation begins.
What annotation precision do you target?+
We target greater than 95% IoU agreement between annotators for standard bounding box tasks. For segmentation masks, we use pixel-level accuracy metrics with senior review on complex objects.
What output formats do you support?+
COCO JSON, Pascal VOC XML, YOLO TXT, TFRecord, and custom formats. Bounding box coordinates in both pixel and normalized formats per your framework requirements.
Can you handle aerial and non-standard sensor images?+
Yes. We annotate imagery from drone cameras, thermal sensors, fisheye lenses, and satellite imagery. Annotator training is customized for each sensor type and object appearance.
How do you handle small and densely packed objects?+
We use high-resolution annotation tools with zoom functionality and enforce minimum IoU standards per instance size tier. Dense packing scenarios use instance-level annotation with crowd flags.
What does a custom object detection dataset cost?+
Projects range from $10K for focused single-class datasets (5,000-20,000 images) to $150K+ for large multi-class, multi-environment collections with segmentation annotation.
Can you guarantee minimum instance counts per class?+
Yes. We track per-class instance counts throughout collection and annotation. Delivery is blocked until all classes meet agreed minimum thresholds.
Get Started
Scope Your Custom Object Detection Dataset
Share your object taxonomy, sensor configuration, and annotation requirements. A computer vision data specialist will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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