Multi-Stream Feature Fusion for Computer Vision
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Solution Overview
Problem
Conventional computer vision systems face challenges in object detection in outdoor scenarios with high dynamic range illumination due to treating exposure control and perception as independent tasks, leading to failure in maintaining crucial features for robust detection in high contrast scenes.
Innovation Solution
A neural exposure fusion approach that combines standard dynamic range captures in the feature domain, embedded in an end-to-end trainable vision pipeline, jointly learning exposure control, image processing, feature extraction, and detection using a downstream loss function, and generating multiple raw exposure-specific images for enhanced feature extraction and fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conventional HDR fusion methods are used, then high dynamic range imaging is achieved, but detection accuracy deteriorates due to underexposed or overexposed regions
Solution Approach 1:
The patent segments the HDR image processing into multiple exposure-specific image streams (e.g., low exposure, high exposure streams) that are processed independently through separate neural networks. Each stream handles specific illumination ranges, avoiding the underexposed/overexposed problems of conventional single-HDR pipelines while maintaining comprehensive dynamic range coverage.
Solution Approach 2:
The patent transitions from fusing images in the pixel domain to fusing features in the feature domain. By extracting features from multiple exposure-specific images and combining them at the feature level rather than the pixel level, the system achieves more robust detection accuracy while preserving the benefits of HDR illumination handling.
2Ease of manufacture
If exposure control and perception are treated as independent tasks, then each task can be optimized separately, but feature preservation for robust detection deteriorates
Solution Approach 1:
The patent merges exposure control and perception into a unified end-to-end trainable neural network pipeline. The exposure control module and perception module share computational graphs and training data, allowing joint optimization that preserves crucial features for robust detection while maintaining separate functional capabilities.
Solution Approach 2:
The neural network pipeline is designed as a universal system that simultaneously performs exposure control, image processing, feature extraction, and object detection. This multi-functional approach ensures that features are preserved across all processing stages while each function benefits from the others' optimizations.
3Measurement precision
If feature-domain fusion is implemented, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by processing only the necessary portions of the image data through multiple exposure-specific streams rather than processing the entire image uniformly. The system selectively applies high-computation feature extraction and fusion only where needed, reducing overall computational complexity while maintaining high detection accuracy in critical regions.
Data Source
AI summary
A computer-vision pipeline is organized as a closed loop of a sensor-processing phase, an image-processing phase, and an object-detection phase, each comprising a respective phase processor coupled to a master processor. The sensor-processing phase creates multiple exposure images, and derives multi-exposure multi-scale zonal illumination-distributions, to be processed independently in the image-processing phase. In a first implementation of the object-detection phase, extracted exposure-specific features are pooled prior to overall object detection. In a second implementation, exposure-specific objects, detected from the exposure-specific features, are fused to produce the sought objects of a scene under consideration. The two implementations enable detecting fine details of a scene under diverse illumination conditions. The master processor performs loss-function computations to derive updated training parameters of the processing phases. Several experiments applying a core method of operating the computer-vision pipelines, and variations thereof, ascertain performance gain under challenging illumination conditions.