Cross-Trained CNN for Depth-Enhanced Object Recognition
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Solution Overview
Problem
Conventional convolutional neural networks (CNNs) face inefficiencies when learning correlations between color and depth modalities, particularly when depth images are absent during testing, leading to poor performance in tasks like object recognition.
Innovation Solution
A cross-trained CNN method is employed, where a pre-trained CNN is fine-tuned using depth images to create a depth CNN, and then further fine-tuned using color images to generate a depth-enhanced color CNN, allowing the network to extract and combine features effectively even when depth modality is unavailable during testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CNNs are used to process color and depth images separately with late fusion, then the system structure remains simple, but the learning efficiency of correlations between modalities deteriorates and performance drops when depth images are absent during testing
Solution Approach 1:
The patent applies preliminary action by pre-training the CNN on color images before fine-tuning with depth images. This sequential approach allows the network to first learn from the more abundant color data, then adapt to depth modality, improving robustness when depth is unavailable during testing without requiring complex simultaneous multi-modal training structures
Solution Approach 2:
The training process is segmented into distinct phases: initial pre-training on color images, followed by fine-tuning on depth images, and finally cross-training with both modalities. This segmentation allows each phase to focus on specific modality characteristics, improving learning efficiency without overwhelming the network with complex simultaneous multi-modal processing
2Ease of manufacture
If late fusion technique is used to combine color and depth features separately, then the implementation remains straightforward, but the correlation learning between modalities becomes inefficient
Solution Approach 1:
The patent merges color and depth feature processing into a unified CNN architecture that shares convolutional layers. Instead of separate processing pipelines with late fusion, the network jointly processes both modalities through shared early layers, enabling efficient correlation learning while maintaining implementation simplicity through a single integrated model
3Quantity of substance
If the CNN is trained only on color images, then the training data requirement is reduced, but the performance deteriorates when depth information is available during testing
Solution Approach 1:
The CNN is designed with multi-functionality through cross-training on multiple modalities. The network can effectively process both color and depth images due to fine-tuning on both types of data, making it universally applicable regardless of which modality is available during testing, thereby improving reliability without requiring exponentially more training data
Data Source
AI summary
Embodiments of a computer-implemented method for training a convolutional neural network (CNN) that is pre-trained using a set of color images are disclosed. The method comprises receiving a training dataset including multiple multidimensional images, each multidimensional image including a color image and a depth image; performing a fine-tuning of the pre-trained CNN using the depth image for each of the plurality of multidimensional images; obtaining a depth CNN based on the pre-trained CNN, wherein the depth CNN is associated with a first set of parameters; replicating the depth CNN to obtain a duplicate depth CNN being initialized with the first set of parameters; and obtaining a depth-enhanced color CNN based on the duplicate depth CNN being fine-tuned using the color image for each of the plurality of multidimensional images, wherein the depth-enhanced color CNN is associated with a second set of parameters.