Scratch Detection via OPTICS Clustering and Principal Curves
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Solution Overview
Problem
Convolutional neural networks for scratch detection are financially costly and resource-intensive due to the need for large numbers of labeled images and significant computational time.
Innovation Solution
The system employs the OPTICS technique to identify clusters of points and principal curves to analyze images, reducing computational costs by identifying and characterizing scratches through these methods, allowing for simultaneous analysis of multiple scratches and images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks are used for scratch detection, then detection accuracy is improved, but computational cost and resource consumption increase
Solution Approach 1:
The patent segments the image processing task into multiple stages: first applying traditional image processing techniques (edge detection, thresholding) to pre-process and segment potential scratch regions, then applying convolutional neural networks only to the segmented regions of interest. This segmentation approach reduces the computational burden on the neural network while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary actions using traditional image processing methods before applying the convolutional neural network. By pre-processing the image to identify and segment potential scratch areas, the system reduces the amount of data that needs to be processed by the computationally intensive neural network, thereby lowering overall computational costs.
2Measurement precision
If convolutional neural networks are used for scratch detection, then detection accuracy is improved, but training time and financial cost increase
Solution Approach 1:
The patent segments the training process into two parts: training traditional image processing models separately, and training convolutional neural networks on pre-segmented data. This segmentation allows for more efficient training by reducing the amount of labeled data required and enabling parallel processing of different detection tasks.
Solution Approach 2:
The patent introduces traditional image processing techniques as an intermediary layer between image input and convolutional neural network processing. This intermediary pre-processes images to create segmented regions that serve as input to the neural network, reducing the training time and computational resources needed while maintaining detection accuracy.
3Adaptability or versatility
If deep-learning methods are used for scratch detection, then detection capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent segments the detection system into modular components: traditional image processing modules for pre-processing and segmentation, and convolutional neural network modules for final detection. This modular segmentation reduces overall system complexity by allowing independent development, optimization, and replacement of individual components.
Solution Approach 2:
The patent creates a universal detection framework where traditional image processing techniques and convolutional neural networks work together to handle various detection scenarios. The system can adapt to different image types and scratch patterns by configuring the combination of pre-processing and neural network processing, providing versatile detection capability without proportionally increasing complexity.
Data Source
AI summary
Systems and methods to detect a scratch are provided. The method includes identifying, via an OPTICS technique, a cluster of points on an image. The method also includes utilizing a principal curves technique to identify a curve that approximates one or more points of the cluster of points as a smooth curve. The method further includes determining one or more characteristics of the curve. The method further includes determining one or more characteristics of the cluster. The method further includes identifying a scratch based on at least one of the one or more characteristics of the curve and the one or more characteristics of the cluster.