Instance Segmentation Error Prediction for Uniform Object Masks
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Solution Overview
Problem
Existing instance segmentation methods struggle to generate masks of uniform quality for objects, especially when dealing with occlusions and unseen objects, and lack effective error prediction and correction mechanisms.
Innovation Solution
A system and method that utilizes an instance segmentation model to recognize objects, predicts errors using an error prediction model, and corrects these errors to improve mask generation, incorporating a foreground map, center map, and offset map for enhanced segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instance segmentation models are trained on large-scale synthetic data, then segmentation capability for occluded objects is improved, but mask quality uniformity across different objects deteriorates
Solution Approach 1:
The patent implements error prediction models that analyze segmentation results and generate correction maps. These models process the initial segmentation masks, predict error locations and magnitudes, and generate corrected masks through iterative refinement. The feedback mechanism ensures uniform mask quality by identifying and correcting errors in previously segmented objects, thereby resolving the quality uniformity issue while preserving segmentation capability.
Solution Approach 2:
The patent applies error prediction and correction in a multi-stage process. Before final mask generation, the system performs preliminary error analysis on segmentation results, predicts potential errors using trained models, and generates correction maps. This preliminary action prevents error propagation and ensures consistent mask quality across all objects, including occluded and unseen objects.
2Manufacturing precision
If error prediction models are integrated into instance segmentation systems, then mask quality uniformity is improved, but system complexity increases
Solution Approach 1:
The patent designs error prediction models that serve multiple functions: they predict segmentation errors, generate correction maps, and refine masks across different object types. The same model architecture handles various object categories, occlusion levels, and segmentation scenarios, reducing the need for separate specialized components and thereby managing system complexity while improving mask quality uniformity.
3Measurement precision
If multiple maps (foreground, center, offset) are used for segmentation, then segmentation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent divides the segmentation task into multiple specialized components: foreground maps identify object regions, center maps locate object centers, and offset maps provide distance transformations. Each map is processed through dedicated error prediction and correction models, allowing parallel processing and specialized optimization. This segmentation of processing tasks improves segmentation accuracy while managing complexity through modular architecture.
Data Source
AI summary
A method of improving instance segmentation is provided, the method including: receiving at least one of an image or a depth map; recognizing one or more objects from at least one of the image or the depth map based on an instance segmentation model to generate an estimation for the instance segmentation; predicting errors within the estimation based on an error prediction model; and correcting the estimation based on the predicted errors to improve the instance segmentation to generate a mask corresponding to the one or more objects.