Image Detection Device Using Block Calibration for Inclined Objects
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Solution Overview
Problem
Current image detection technologies consume significant computation resources and are inefficient in inspecting large numbers of physical objects, leading to unsatisfactory performance and misjudgments due to the inability to accurately compare image features, especially when objects are inclined.
Innovation Solution
An image detection device and method that captures images, transforms them into binary images, computes tilt angles, and uses minimum bounding rectangles to calibrate and compare image blocks with standard images, reducing misjudgments by aligning image features with standard orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the image detection device analyzes the image features of the entire object image to estimate the quality of the physical object, then the quality estimation can be performed, but it consumes significant computation resources
Solution Approach 1:
The patent divides the entire object image into multiple image blocks (first image blocks and second image blocks) based on contour detection. By segmenting the image into smaller regions and selectively processing only certain blocks (e.g., those containing contours or features of interest), the system reduces the overall computation required while maintaining quality estimation accuracy.
Solution Approach 2:
The patent extracts only the necessary image blocks that contain relevant features for quality assessment, rather than processing the entire image. Specifically, it identifies and processes first image blocks containing contours and second image blocks containing specific features, discarding or skipping blocks that do not contribute to quality estimation, thereby reducing computation resource consumption.
2Measurement precision
If the image detection device compares image features of the entire object image with standard images, then quality estimation can be performed, but it is inefficient when inspecting large numbers of physical objects
Solution Approach 1:
The patent segments both the object image and standard images into corresponding image blocks arranged in arrays. By comparing only the relevant image blocks (rather than entire images) and utilizing the array structure for efficient access and comparison, the system improves inspection efficiency while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary processing by dividing images into blocks and organizing them in arrays before comparison. It pre-identifies which image blocks contain relevant features and prepares them for comparison, so that when actual quality detection is performed, the comparison process is faster and more efficient.
3Device complexity
If the image detection device directly compares image blocks without calibration, then the comparison process is simple, but it causes misjudgment when objects are inclined
Solution Approach 1:
The patent performs preliminary calibration of image blocks by computing tilt angles and applying rotation transformations before comparison. By pre-aligning the image blocks to standard orientations, the system eliminates misjudgment caused by inclined objects. This preliminary action adds some complexity but ensures accurate comparison results.
Solution Approach 2:
The patent changes the orientation parameter of image blocks by computing tilt angles and applying rotation transformations. It adjusts the rotation angle of each image block to align with the standard orientation, thereby eliminating the effect of inclination on comparison accuracy while maintaining a relatively simple overall process.
Data Source
AI summary
An image detection device includes an image capturing module, a storage medium, and a processor. The processor is connected to the image capturing module and the storage medium and configured to obtain a plurality of sample object images of an image of object to be inspected; store the plurality of sample object images to a sample image array, where the sample image array and a standard image file array include same array index and the standard image file array includes a plurality of standard object images; respectively compare the plurality of sample object images of the sample image array and the plurality of standard object images of the standard image file array based on the array index to compute an inference score of each of the plurality of sample object images; and estimate an object quality according to the inference score of the plurality of sample object images.