Panoptic Scenes Segmentation Dataset

Home » Case Study » Panoptic Scenes Segmentation Dataset

Project Overview:

Objective

In this project, we have meticulously developed a comprehensive dataset tailored for panoptic segmentation. This innovative dataset merges the nuances of both semantic and instance segmentation across diverse scenes. Consequently, it marks a significant leap in fields like urban planning, autonomous driving, robotics, and augmented reality.

Scope

Our team’s dedicated efforts have culminated in the acquisition of an extensive array of images, depicting a variety of scenes including urban, rural, indoor, and natural landscapes. Moreover, each image in our dataset comes with detailed pixel-wise annotations, precisely differentiating and outlining every object and background element.

Sources

Collaborative efforts with city councils for urban and traffic scenes.
Additionally, forming partnerships with natural reserves and national parks allows us to capture the essence of nature more effectively.
Furthermore, leveraging open-source imagery platforms will provide us with a wider perspective.
To increase our reach even more, we encourage crowdsourced contributions through our dedicated applications.

Data Collection Metrics

Total Scenes Collected: 25,000
Urban Scenes: 10,000
Rural Landscapes: 4,000
Indoor Settings: 6,000
Natural Environments: 5,000
Total Scenes Annotated: 20,000

Annotation Process

Stages

Image Pre-processing: To ensure each image meets our high standards of quality, we perform color balancing, sharpening, and normalization.
Pixel-wise Segmentation: Equipped with the latest tools, our annotators meticulously label every pixel, thereby creating a comprehensive segment map.
Validation: A secondary team of experts then reviews the annotations to ensure utmost accuracy.

Annotation Metrics

Total Pixel-wise Annotations: 20,000
Average Annotation Time per Scene: 40 minutes, reflecting the intricacy of our work.

Quality Assurance

Stages

Automated Model Assessment: We employ cutting-edge models to ensure annotation accuracy. Consequently, this allows us to identify and correct any discrepancies efficiently.
Peer Review: Additionally, experienced annotators cross-check a subset of images for consistency. This step further guarantees the reliability of our annotations.
Inter-annotator Agreement: Moreover, we randomly select images for multiple annotators to review. This practice helps us establish a standard of excellence and maintain high-quality annotations.

QA Metrics

Annotations Verified using Models: 12,500 (50% of total annotated scenes)
Additionally, we have Peer-reviewed Annotations: 8,000 (40% of total annotated scenes)
Moreover, we identified Inconsistencies Identified and Rectified: 1,000 (5% of total annotated scenes)

Conclusion

The Panoptic Scenes Segmentation Dataset represents a revolutionary stride in the domain of computer vision. By encompassing a broad spectrum of scenarios, ranging from bustling urban streets to serene natural vistas, this dataset aims to become the cornerstone for next-generation visual recognition systems. Its precision and depth promise to propel forward applications in urban planning, autonomous mobility, and beyond.