Segment-Aware Semantic Segmentation for Aerial Imagery
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Solution Overview
Problem
Current semantic segmentation methods, based on Deep Convolutional Neural Networks, often overlook pixel-segment association during training and validation, leading to inferior classification performance, particularly in detecting segments rather than individual pixels, as seen in the failure to identify all tanks in an aerial image.
Innovation Solution
A computer-implemented method and system that computes segment-aware loss and metric values by using a segment ID mask to associate pixels with their respective segments, optimizing the machine learning model to improve segment-based recall and precision, by calculating per-segment values and metrics, and calibrating these values based on segment identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If per-pixel loss calculation is used for training, then classification accuracy of individual pixels is improved, but segment-based recall deteriorates
Solution Approach 1:
The patent divides the image into distinct segments and calculates loss separately for each segment rather than treating all pixels uniformly. This segment-level segmentation allows the model to optimize for both pixel accuracy and segment recall by evaluating performance metrics at the segment level, thereby resolving the contradiction between pixel-level precision and segment-level recall.
Solution Approach 2:
The patent applies different loss calculation strategies to different segments based on their specific characteristics. By computing per-segment loss values and applying segment-specific weights, the model can prioritize accurate classification in some segments while ensuring complete detection in others, thus achieving both pixel accuracy and segment recall simultaneously.
2Productivity
If traditional DCNN models are used for semantic segmentation, then processing speed is maintained, but detection of all segments deteriorates
Solution Approach 1:
The patent introduces segment-aware loss calculation and segment-based metric computation that provides feedback to the DCNN model during training. This feedback mechanism guides the model to optimize for both speed and completeness by evaluating performance at the segment level and adjusting weights accordingly, enabling the model to detect all segments while maintaining efficient processing.
Solution Approach 2:
The patent modifies the training parameters by introducing segment-specific weights and loss functions that emphasize complete segment detection. By changing the optimization parameters to include segment-level metrics, the model learns to prioritize detection completeness without sacrificing processing speed, as the underlying DCNN architecture remains efficient.
3Device complexity
If uniform weight is applied to all pixels in loss calculation, then computational simplicity is maintained, but classification accuracy of specific segments deteriorates
Solution Approach 1:
The patent applies different weights to different segments based on their classification difficulty and importance. By computing segment-specific weights and applying them to the loss function, the model can focus computational resources on difficult-to-classify segments while maintaining simplicity in easy segments, thus improving overall segment classification accuracy without excessive computational overhead.
Solution Approach 2:
The patent applies excessive weighting to segments that are difficult to classify or critically important, rather than using uniform weights for all segments. This partial focus on specific segments allows the model to achieve higher accuracy for challenging cases while keeping the overall computational complexity manageable by not over-engineering the solution for all pixels equally.
Data Source
AI summary
Updating a machine learning model for semantic segmentation by receiving an image and a segment ID mask, the image having a plurality of pixels, and the segment ID mask containing for each pixel of the image one of a plurality of segment identifiers identifying a segment to which the pixel belongs, feeding the image to a machine learning model and computing an outcome, computing a per-segment value for each one of the plurality of segment identifiers from the outcome and from pixels associated with the one of the plurality of segment identifiers, aggregating a plurality of per-segment values to create a segment-aware value and using the segment-aware value for training, validating and testing the machine learning model.