Wavelet Transformer Neural Network for Efficient Feature Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for detecting image features in autonomous vehicles are not computationally or power-efficient, as they process high-resolution image data without effective compression, leading to increased processing requirements and power consumption.
Innovation Solution
A system that uses a wavelet transformer to decompose image data into frequency sub-bands and an artificial neural network to detect features, with optional filtering to remove low-coefficient data and stimulus-based dropout to reduce computations, allowing for efficient processing of lower resolution sub-bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution image data is processed without compression, then image feature detection accuracy is improved, but computational requirements and power consumption increase
Solution Approach 1:
The image data is divided into multiple frequency sub-bands using wavelet transform. The neural network processes only selected sub-bands (e.g., LL, LH, HL) rather than the entire high-resolution image, segmenting the computational workload to reduce power consumption while maintaining detection accuracy for critical features.
Solution Approach 2:
The wavelet transform extracts and separates different frequency components of the image into distinct sub-bands. The system then selectively processes only the relevant sub-bands containing important feature information, extracting essential data while discarding redundant high-frequency noise that consumes computational resources without adding value to feature detection.
2Measurement precision
If high-resolution image data is processed without compression, then image feature detection accuracy is improved, but computational requirements increase
Solution Approach 1:
The image processing task is segmented into multiple frequency sub-bands. The neural network processes only the necessary sub-bands in parallel, dividing the computational burden and improving processing efficiency while maintaining the ability to detect features across different frequency ranges.
Solution Approach 2:
The system performs partial processing by selecting and processing only the most relevant frequency sub-bands rather than analyzing the entire high-resolution image. This partial action approach achieves sufficient feature detection accuracy with reduced computational requirements, avoiding excessive processing of redundant data.
3Measurement precision
If all frequency sub-bands are processed, then image feature detection completeness is improved, but computational complexity increases
Solution Approach 1:
Different frequency sub-bands are processed with different levels of computational resources based on their importance. The LL (low-low) sub-band containing primary image information receives full processing attention, while other sub-bands are processed selectively. This local quality approach optimizes computational complexity by allocating resources according to the informational value of each sub-band.
Solution Approach 2:
The system performs partial processing of frequency sub-bands by selecting only those sub-bands that contain relevant feature information for the specific detection task. This avoids the excessive computational complexity of processing all sub-bands uniformly, while still achieving complete detection of important features through selective sub-band analysis.
Data Source
AI summary
Systems and methods are provided for detecting features from multi-modal image-like data representations. The system includes a wavelet transformer configured to, via at least one processor, receive image data and to wavelet transform the image data, thereby providing decomposed image data divided into frequency sub-bands. The system further includes an artificial neural network configured to receive and process at least one sub-band of the decomposed image data to detect image features based thereon, the artificial neural network configured to output the detected image features.