Redundant Feature Detection Engine for Image Segmentation
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Solution Overview
Problem
Traditional neural networks in computer vision systems face challenges in accurately predicting features when objects span multiple receptive fields or grid cells, leading to inconsistencies and reduced detection accuracy, especially in scenarios like autonomous driving where precise localization is critical.
Innovation Solution
A redundant feature detection engine is introduced, which segments images into grid cells, predicts object codes uniquely identifying objects, and aggregates predictions from multiple cells to merge feature outputs, ensuring robustness and consistency through a constrained loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional neural networks process images by dividing into individual receptive fields, then processing efficiency is improved, but detection accuracy deteriorates when objects span multiple grid cells
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple grid cells, where each cell is processed independently by the neural network to generate local predictions. This maintains processing efficiency while enabling redundant detection across cells that an object may span.
Solution Approach 2:
The patent merges predictions from multiple grid cells by clustering cells that detect the same object and combining their prediction outputs. This resolves inconsistencies and improves detection accuracy for objects that span multiple receptive fields.
2Speed
If predictions are made independently for each grid cell, then processing speed is improved, but prediction consistency deteriorates
Solution Approach 1:
The patent implements feedback through a loss function that enforces consistency constraints on predictions across different grid cells. This ensures that independent predictions maintain coherence and agree on object properties, resolving the consistency issue while preserving processing speed.
3Reliability
If feature predictions are aggregated from multiple grid cells, then detection robustness is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary clustering of grid cells based on object codes before aggregating feature predictions. This organization reduces the computational burden of merging predictions by pre-grouping cells that detect the same object, thereby maintaining robustness while reducing complexity.
Data Source
AI summary
An approach is provided for a redundant feature detection engine. The approach, for instance, involves segmenting an input image into a plurality of grid cells for processing by the redundant feature detection engine. The redundant feature detection engine includes a neural network. The approach also involves, for each of the plurality of grid cells, initiating a prediction of an object code by the redundant feature detection engine. The object code is a predicted feature that uniquely identifies an object depicted in the input image. The approach further involves aggregating the plurality of grid cells into one or more clusters based on the object code predicted for said each grid cell. The approach further involves predicting one or more features of the object corresponding to a respective cluster of the one or more clusters by merging one or more feature prediction outputs of said each grid cell in the respective cluster.