Pruned CNN Filters for Embedded Environmental Hazard Recognition
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Solution Overview
Problem
Convolutional neural networks (CNNs) for image recognition require significant computational and power resources, making them impractical for embedded and mobile applications, as simply compressing or pruning weights does not adequately reduce the complexity of deep neural networks.
Innovation Solution
A pruned CNN is developed by removing filters with small weights or low significance from the convolutional layers, which reduces the number of convolution operations, thereby decreasing computational and power requirements, allowing the CNN to be implemented in resource-constrained devices without compromising accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If filters are removed from CNN layers to reduce computational complexity, then the CNN becomes more suitable for embedded and mobile applications, but image recognition accuracy may deteriorate
Solution Approach 1:
The patent extracts and removes redundant filters from CNN layers based on their contribution to image recognition accuracy. By identifying and eliminating filters with minimal impact on performance, the system reduces computational complexity while preserving essential recognition capabilities, directly resolving the contradiction between simplification and accuracy maintenance.
Solution Approach 2:
The patent changes the parameter configuration of the CNN by dynamically adjusting the number and distribution of filters across layers. Through iterative pruning and retraining, the system optimizes filter retention rates to achieve the minimum configuration necessary for accurate image recognition, thereby reducing complexity without sacrificing performance.
2Measurement precision
If deeper CNNs with more parameters are used to improve image recognition accuracy, then recognition precision increases, but computational and power resources required increase
Solution Approach 1:
The patent extracts and eliminates redundant computational operations by removing unnecessary filters from the CNN architecture. This reduction in the number of convolution operations directly decreases power consumption while maintaining recognition accuracy through selective retention of high-contribution filters.
Solution Approach 2:
The patent optimizes the parameter configuration of the CNN by adjusting filter counts and layer depths to achieve the minimum necessary complexity for accurate recognition. This parameter optimization reduces computational load and power consumption while preserving essential recognition functionality.
3Measurement precision
If CNNs with more filters are used to improve feature recognition, then image recognition accuracy improves, but the number of computational operations increases
Solution Approach 1:
The patent extracts and removes redundant filters that contribute minimally to feature recognition accuracy. By eliminating these low-contribution filters, the system reduces the number of computational operations while preserving the essential feature extraction capabilities needed for accurate image recognition.
Solution Approach 2:
The patent optimizes the filter configuration parameters of the CNN to achieve the minimum necessary number of filters for accurate feature recognition. Through iterative pruning and performance evaluation, the system identifies the optimal filter count that balances recognition accuracy with computational efficiency.
Data Source
AI summary
Systems and methods for predicting changes to an environment, including a plurality of remote sensors, each remote sensor being configured to capture images of an environment. A processing device is included on each remote sensor, the processing device configured to recognize and predict a change to the environment using a pruned convolutional neural network (CNN) stored on the processing device, the pruned CNN being trained to recognize features in the environment by training a CNN with a dataset and removing filters from layers of the CNN that are below a significance threshold for image recognition to produce the pruned CNN. A transmitter is configured to transmit the recognized and predicted change to a notification device such that an operator is alerted to the change.