Placement Detection System Using Dynamic Threshold Binarization
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Solution Overview
Problem
Existing systems face challenges in detecting the shape and orientation of objects placed in a region without requiring users to hold or cover the object, especially when external light influences the detection process.
Innovation Solution
A placement detection system utilizing a CMOS or CCD image sensor and a controller that binarizes images using threshold values to differentiate between object and background, and dynamically adjusts threshold values to mitigate the impact of external light, allowing for accurate detection of object placement orientation without the need for covers or holding mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a cover is used to hold down the object for covering the placement region, then detection precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces the mechanical cover system with an optical/image processing system. Instead of physically covering the placement region to detect object presence, the system captures an image of the placement region and uses image processing (binarization, contour detection) to detect the object's shape and orientation, thereby eliminating the need for mechanical covers while maintaining detection precision
Solution Approach 2:
The patent introduces an image processing algorithm as an intermediary between the camera and the detection result. The algorithm processes the captured image through binarization and contour detection to extract object information, serving as a mediator that translates visual data into detection results without requiring physical contact or covers
2Reliability
If threshold values are adjusted to mitigate external light impact, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent dynamically adjusts the threshold parameter in the binarization process based on the captured image characteristics. By changing the threshold value adaptively, the system compensates for variations in external lighting conditions and maintains reliable detection without requiring complex hardware modifications
Solution Approach 2:
The patent implements a feedback mechanism where the image processing results are used to adjust subsequent processing parameters. The system analyzes the captured image, determines appropriate threshold values, and applies them in real-time processing, creating a closed-loop system that improves reliability through continuous adaptation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable detection of object shape and orientation even with external light interference, reducing user operational burden and allowing for easy placement of objects without additional support, thus enhancing usability and detection precision.
Implementation Method 1
an image sensor that images a glass surface of the placement table, that is, the placement region, from below the placement table to generate an input image
Data Source
Figure 1A~1B
Figure 2
Figure 3
AI summary
A placement detection system includes: a placement table; an imaging device that images an object placed in a placement region on the placement table through the placement table to generate an input image; and a control device that receives the input image. The control device performs first binarization processing for the input image based on a first threshold value to generate a first binarized image and determines whether the object is placed in a predetermined placement orientation based on the first binarized image. The control device changes a threshold value for a target pixel to a second threshold value higher than the first threshold value when the object is determined to be placed in the predetermined placement orientation, the target pixel being sandwiched between pixels having pixel levels lower than or equal to the first threshold value and has a higher pixel level than the first threshold value in a first region of the input image, performs second binarization processing for the target pixel based on the second threshold value to generate a second binarized image, and detects a shape of the object based on the second binarized image.