Thermal Cup Surface Defect Detection with Adaptive Image Segmentation
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Solution Overview
Problem
Existing methods for detecting surface defects in thermal cups, such as pits and polishing prints, are inefficient and costly due to reliance on human inspection and inadequate vision algorithms, especially for area-array cameras, and require frequent adjustments in lighting and camera configuration for different defect types.
Innovation Solution
A method involving image preprocessing, convolution operations using Gaussian derivatives, and threshold segmentation to enhance contrast and accurately locate defects, including pit and polishing print regions, while adjusting parameters based on illumination gradients to maintain efficiency across varying environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual detection by human eyes is used, then detection can be performed with simple equipment, but detection efficiency is low and many missed detections occur
Solution Approach 1:
The patent replaces manual visual inspection with an automated vision detection system using area-array cameras and image processing algorithms. The system captures images of thermal cup surfaces and uses computer vision techniques to automatically identify defects such as pits and polishing prints, eliminating the need for human operators while improving both detection efficiency and accuracy.
Solution Approach 2:
The detection system performs self-assessment by automatically analyzing images and identifying defects without requiring human intervention. The algorithm processes images, detects defect patterns, and generates detection results autonomously, enabling the system to serve itself in the detection task.
2Adaptability or versatility
If vision algorithms are developed for area-array cameras to detect all defect types, then detection coverage is improved, but algorithm complexity and detection cost increase
Solution Approach 1:
The patent segments the defect detection task into distinct categories (pits, polishing prints, and other defects) and develops specialized processing branches for each type. The system uses different image processing techniques and detection algorithms for different defect types, allowing comprehensive coverage while managing algorithm complexity through modular design.
Solution Approach 2:
The patent creates a universal detection algorithm framework that can handle multiple defect types using a single area-array camera system. The algorithm is designed to be multi-functional, capable of detecting pits, polishing prints, and other surface defects through a unified processing pipeline with adjustable parameters, eliminating the need for separate detection systems for each defect type.
3Measurement precision
If lighting and camera configuration are adjusted for each defect type, then detection precision is improved, but detection time and operational complexity increase
Solution Approach 1:
The patent performs preliminary image processing operations such as gamma correction and contrast enhancement on all captured images before defect-specific analysis. This preliminary processing optimizes image quality in advance, allowing the system to detect different defect types without requiring subsequent adjustments to lighting or camera configuration, thereby saving time.
Solution Approach 2:
The patent changes image processing parameters such as gamma values and contrast enhancement levels dynamically during the detection process based on the specific defect type being analyzed. Rather than adjusting physical lighting or camera settings, the system modifies software parameters to optimize detection precision for different defects, maintaining operational efficiency.
Data Source
AI summary
A method for detecting a surface defect of a thermal cup, a system thereof, a device and a medium are provided. The method includes: acquiring thermal cup images from different angles, and preprocessing thermal cup images to generate enhanced thermal cup images; performing convolution operation for a first-order derivative of a Gaussian function on the enhanced thermal cup images to determine first filtered images; and determining defect regions according to the first filtered images and the thermal cup images by using a threshold segmentation method based on a bilinear interpolation and a second-order derivative of the Gaussian function. The set parameters are adjusted based on a gradient change and the current illumination environment. The defect regions include a final pit defect region, an upper side polishing print defect region and a lower side polishing print defect region.