Panoptic Segmentation Neural Network for Unknown Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current panoptic segmentation systems fail to accurately detect and classify unknown objects in digital images, often inaccurately grouping them as background, and are limited by their closed-set nature, leading to inflexible and computationally costly operations.
Innovation Solution
A panoptic segmentation neural network is developed to discover and classify both known and unknown object instances, generating unknown object subclasses and refining the network to improve detection and classification of unknown objects, enabling open-set panoptic segmentation and expanding the range of detectable objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current panoptic segmentation systems are used to classify objects in digital images, then known objects and background classes can be identified, but unknown objects fail to be detected and are inaccurately grouped as background
Solution Approach 1:
The system segments the classification space into known object classes, unknown object classes, and background classes. By introducing a separate unknown object class category, the system can identify and classify unknown objects independently rather than incorrectly grouping them as background, thereby improving both classification accuracy and adaptability to unseen objects.
Solution Approach 2:
The panoptic segmentation system is made dynamic by enabling it to adapt to new object classes during inference. The system can discover unknown object classes on-the-fly and update its classification capabilities without requiring retraining, allowing it to handle both known and unknown objects effectively while maintaining accurate classification.
2Adaptability or versatility
If panoptic segmentation systems are designed to handle open-set scenarios with unknown objects, then detection flexibility improves, but computational cost and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-defining a set of known object classes and preparing the neural network architecture to handle both known and unknown classes. During inference, the system is already configured to identify unknown objects through the separate unknown class category, eliminating the need for complex runtime adaptations or multiple network configurations.
Solution Approach 2:
The panoptic segmentation system achieves universality by being capable of handling both known object classes and unknown object classes within a single unified framework. The system performs multiple functions simultaneously: classifying known objects, identifying unknown objects, and segmenting background, all through one neural network architecture without requiring separate specialized systems.
3Ease of manufacture
If traditional panoptic segmentation approaches are used, then training on standard datasets is straightforward, but the system remains limited by closed-set constraints and cannot generalize to unseen object classes
Solution Approach 1:
The system implements self-service by automatically discovering and adapting to unknown object classes during inference without requiring external intervention or retraining. The neural network autonomously identifies unknown objects through the dedicated unknown class category and can update its classification behavior on-the-fly, maintaining training simplicity while achieving open-set adaptability.
Data Source
AI summary
This disclosure describes one or more implementations of a panoptic segmentation system that generates panoptic segmented digital images that classify both known and unknown instances of digital images. For example, the panoptic segmentation system builds and utilizes a panoptic segmentation neural network to discover, cluster, and segment new unknown object subclasses for previously unknown object instances. In addition, the panoptic segmentation system can determine additional unknown object instances from additional digital images. Moreover, in some implementations, the panoptic segmentation system utilizes the newly generated unknown object subclasses to refine and tune the panoptic segmentation neural network to improve the detection of unknown object instances in input digital images.