What does the Doors Detection YOLO Dataset include?

The dataset includes 1,500 high-quality images annotated with doors, doorways, and architectural structures. It is split into 85% training, 10% validation, and 5% testing subsets, optimized for real-time YOLO detection models.

How was the dataset pre-processed and augmented?

All images were auto-oriented, resized to 640×640 pixels, and standardized for model efficiency. Augmentations such as flips, rotations, crops, and grayscale transformations were applied to enhance robustness and diversity.

Does GTS ensure dataset quality and compliance?

Yes. GTS follows ISO 9001:2015 and ISO 27001:2013 certifications, maintains strict QC and rework processes, and ensures compliance with GDPR, HIPAA, and international data protection standards before delivery.

Doors Detection YOLO Dataset

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Doors Detection YOLO Dataset

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Doors Detection YOLO Dataset

Use Case

Doors Detection YOLO Dataset

Description

Advance your real-time door detection models with our meticulously curated YOLO Doors Detection Dataset. Featuring high-quality images.

Description:

This dataset comprises 1,500 high-quality annotated images, specifically curated to enhance real-time door detection using YOLO (You Only Look Once) models. The dataset is meticulously split into three subsets: 85% for training, 10% for validation, and 5% for testing, ensuring an effective balance for model evaluation and fine-tuning.Each image has been carefully preprocessed for consistency and performance. Images have undergone auto-orientation correction, ensuring all doors are upright, and have been uniformly resized to 640×640 pixels, a resolution optimized for efficient model training while preserving essential details.

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Data Preprocessing and Augmentation

The dataset undergoes a rigorous preprocessing pipeline. For example, it includes:

Auto-orientation correction
Uniform resizing for consistency
Data augmentation techniques such as:
- Flipping
- Rotation
- Cropping
- Grayscale conversion

Annotations and Data Quality

Each image is carefully annotated to include:

Doors
Doorways
Related structural elements

In addition, the annotations follow high-quality standards. Therefore, they support accurate object detection and localization tasks.

Key Features of the Dataset

1,500 high-quality annotated images
Optimized resolution for YOLO models (640×640)
Balanced dataset split for training and evaluation
Advanced augmentation techniques
Accurate and detailed object annotations

Applications of Door Recognition Dataset

The Door Detection Dataset can be used in several practical applications. For instance:

Smart Surveillance Systems: Detect doors in security setups
Robotics and Automation: Enable navigation in indoor environments
Computer Vision Research: Develop and benchmark detection models
Smart Buildings: Improve automation and accessibility systems

Conclusion

The Door recognition Dataset is a valuable resource for developing efficient object detection systems. Overall, it provides well-structured and high-quality data for training reliable models. More importantly, it supports real-time applications in surveillance, automation, and smart environments.

This dataset is sourced from Kaggle.

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Doors Detection YOLO Dataset