CNN Model Pruning for Surface Defect Detection Speed
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Solution Overview
Problem
Current product surface defect detection methods, relying on visual inspection or traditional convolutional neural networks, face challenges in meeting the increased speed and accuracy demands of modern production processes due to high missed detection rates and long detection times.
Innovation Solution
A network-based collaborative pruning method is developed to construct an efficient product surface defect detection model by evaluating the importance of convolution kernels, using genetic algorithms to optimize the network architecture, and performing micro-training to reduce redundant circuits and improve detection speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional convolutional neural network is used for product surface defect detection, then detection accuracy is improved, but detection time increases
Solution Approach 1:
The patent extracts and removes redundant convolution kernels from the traditional CNN model through importance evaluation and pruning. By identifying and eliminating kernels that contribute minimally to detection accuracy, the model achieves faster inference speed while maintaining high detection accuracy, thus resolving the contradiction between accuracy and detection time
Solution Approach 2:
The patent changes the model parameters by optimizing the architecture through genetic algorithms and adjusting the number and configuration of convolution kernels. This parameter optimization enables the model to achieve better performance with reduced computational complexity, balancing detection accuracy and speed
2Measurement precision
If traditional convolutional neural network with many parameters is used, then detection accuracy is improved, but model complexity increases
Solution Approach 1:
The patent removes redundant parameters and convolution kernels from the CNN model through systematic pruning. By evaluating kernel importance and eliminating unnecessary components, the model complexity is reduced while preserving the essential features needed for accurate defect detection
Solution Approach 2:
The patent segments the CNN model into different functional modules and applies selective pruning to each segment. This modular approach allows for targeted optimization, reducing overall model complexity while maintaining detection accuracy in critical regions
3Ease of manufacture
If manual visual inspection method is used, then detection cost is reduced, but missed detection rate increases
Solution Approach 1:
The patent implements an automated detection system that performs defect detection without human intervention. The pruned CNN model autonomously identifies surface defects, eliminating the need for manual visual inspection and thereby reducing both labor costs and missed detection rates associated with human fatigue
Data Source
AI summary
A method for constructing an efficient product surface defect detection model based on network collaborative pruning is provided. According to an initial product surface defect detection model, the method provides a network-based collaborative pruning method and constructs the efficient product surface defect detection model. On a premise of ensuring an accuracy of a product defect detection method, a product surface defect detection time is reduced to satisfy manufacturer's requirements on the product surface defect detection time and accuracy of product surface defects.