EfficientPS Architecture
EfficientPS overcomes the limitations of earlier panoptic segmentation by adding innovation that integrates instances and semantic segmentation more effectively.
Step-by-step working of EfficientPS is provided below:
Step 1: Shared Backbone
EfficientPS starts with a shared backbone, which serves as the foundation for both instance and semantic segmentation tasks. This shared backbone extracts essential features from the input images, providing a common basis for subsequent processing.
Step 2: Two-Way Feature Pyramid Network (FPN)
EfficientPS incorporates a two-way FPN that facilitates communication between the shared backbone and the instance and semantic heads. This bidirectional FPN ensures that relevant features are propagated efficiently across different network layers, enhancing the model’s ability to capture fine details and spatial information.
Step 3: Instance and Semantic Heads
EfficientPS utilizes separate instance and semantic heads, each comprising three modules designed to capture fine features and improve segmentation accuracy. These specialized heads focus on refining the extracted features and generating precise masks for individual object instances and semantic categories.
Step 4: Panoptic Fusion Module
The final step in the EfficientPS architecture is the panoptic fusion module, which combines the outputs from the instance and semantic heads to produce the panoptic segmentation result. This fusion process ensures a seamless integration of instance and semantic information, resulting in a more coherent and accurate scene understanding.
What is Panoptic Segmentation?
Panoptic segmentation is a revolutionary method in computer vision that combines semantic segmentation and instance segmentation to offer a holistic insight into visual scenes. This article will explore the operating principles, essential elements, and wide-ranging uses of panoptic segmentation, showcasing its revolutionary influence on different industries and research areas.
Table of Content
- What is Panoptic Segmentation?
- Importance of Panoptic Segmentation
- How Panoptic Segmentation Works
- Network Architecture
- Loss Functions
- EfficientPS Architecture
- Step 1: Shared Backbone
- Step 2: Two-Way Feature Pyramid Network (FPN)
- Step 3: Instance and Semantic Heads
- Step 4: Panoptic Fusion Module
- Addressing Challenges in Panoptic Segmentation
- Applications of Panoptic Segmentation
- 1. Autonomous Driving
- 2. Robotics
- 3. Surveillance and Security
- 4. Augmented Reality (AR) and Virtual Reality (VR)
- 5. Medical Imaging
- Future Directions : Panoptic Segmentation
- FQAs on Panoptic Segmentation
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