3D CNN Multi-Scale Filters for Object Recognition
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Solution Overview
Problem
Current semantic object recognition techniques using RGB images struggle to accurately identify objects at varying distances due to limitations in feature extraction, requiring separate features for near and far distances, and face challenges with computational and memory resource efficiency, especially in applications like autonomous driving and robotics.
Innovation Solution
The implementation of a 3D convolutional neural network (CNN) with multi-scale filters that apply filters of different spatial sizes to extract features from 3D image data, allowing features from large depths to be used for object recognition at small depths and vice versa, while optimizing computational and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate feature extraction is used for near and far distances, then object recognition accuracy for specific distance ranges is improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The patent implements a universal feature extraction system using 3D CNNs with multi-scale filters that can process objects at all distance ranges simultaneously. The network uses shared convolutional layers and filter banks that adapt to different spatial scales, eliminating the need for separate feature extraction pipelines for near and far objects while maintaining high recognition accuracy across all distances
Solution Approach 2:
The patent changes the spatial scale parameters of filters within the 3D CNN architecture. By employing filter banks with varying kernel sizes and receptive fields, the system dynamically adapts to different distance ranges without requiring separate systems. The multi-scale filters transform the feature extraction process to handle varying object sizes and distances through parameter variation rather than structural duplication
2Measurement precision
If multiple separate feature sets are maintained for different distance ranges, then object recognition accuracy is improved, but memory resource requirements increase
Solution Approach 1:
The patent merges multiple distance-specific feature extraction processes into a unified 3D CNN architecture. By combining near and far object feature extraction into shared convolutional layers with multi-scale filters, the system maintains accurate recognition for all distance ranges while using a single consolidated feature representation in memory, significantly reducing the total memory footprint compared to maintaining separate feature sets
Solution Approach 2:
The unified 3D CNN feature extraction system serves multiple distance ranges simultaneously through its multi-scale filter bank, eliminating the need for separate memory allocations for different distance-specific features. The same feature maps and intermediate representations are reused across different spatial scales, reducing overall memory consumption while maintaining comprehensive object recognition capability
3Measurement precision
If depth-based multi-scale filters are applied, then object recognition accuracy across different distances is improved, but computational resource requirements increase
Solution Approach 1:
The patent segments the computational process into hierarchical stages within the 3D CNN, where early layers perform coarse feature extraction with smaller filters and later layers perform fine-grained analysis with larger filters. This segmentation allows the system to process only relevant features at each computational stage, reducing overall computational load compared to applying all filter scales uniformly across the entire input volume, while still achieving high accuracy through progressive refinement
Solution Approach 2:
The patent dynamically adjusts filter parameters such as kernel size, stride, and receptive field based on the detected object distance and scale. By changing these parameters adaptively rather than using fixed large-scale filters for all inputs, the system reduces unnecessary computational operations on distant objects that require coarser processing, while maintaining high accuracy for near objects that benefit from finer detail analysis
Data Source
AI summary
Techniques related to training and implementing convolutional neural networks for object recognition are discussed. Such techniques may include applying, at a first convolutional layer of the convolutional neural network, 3D filters of different spatial sizes to an 3D input image segment to generate multi-scale feature maps such that each feature map has a pathway to fully connected layers of the convolutional neural network, which generate object recognition data corresponding to the 3D input image segment.