Object Volume Measurement Personnel Interference Removal
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Solution Overview
Problem
Conventional methods for measuring object volume are inefficient due to personnel interference, which can lead to inaccurate results and increased processing time, as they rely on skin color and pose detection, making it difficult to distinguish between object and personnel, especially with ambient light variations.
Innovation Solution
A method that involves obtaining an image, removing background information, converting it to a binary image, detecting the object outline, and determining if it extends beyond the camera's field of view, with the option to remove extending parts using morphological methods to ensure accurate volume measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If skin colour or pose detection using deep learning is used to remove personnel interference, then personnel interference can be detected and removed, but processing time increases and measurement precision decreases due to false detection or missed detection
Solution Approach 1:
The patent segments the image processing into distinct stages: binary image conversion, outline detection, boundary comparison, and selective removal. This segmentation allows the system to focus computational resources only on areas where personnel interference is detected, rather than processing the entire image through complex deep learning models, thereby reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The patent applies preliminary action by first converting the image to a binary image and detecting the object outline before attempting to remove personnel interference. This preliminary processing simplifies the subsequent removal operation and allows for faster execution compared to applying complex deep learning models to the entire original image.
2Reliability
If skin colour detection is used to identify personnel, then personnel can be detected, but measurement precision decreases due to false detection or missed detection from ambient light variations
Solution Approach 1:
The patent extracts the personnel interference detection from skin colour-based methods and replaces it with a geometric approach using binary image conversion and outline detection. This extraction eliminates the dependency on skin colour detection, which is susceptible to ambient light variations, and provides more reliable detection that directly identifies personnel based on their spatial occupation and outline characteristics.
Solution Approach 2:
The patent introduces an intermediary approach by using binary image conversion as a mediator between the original image and the personnel detection process. This binary conversion simplifies the image data into distinct foreground and background regions, making it easier to detect personnel interference without being affected by ambient light variations that plague skin colour-based methods.
3Reliability
If deep learning methods are used to recognize and remove personnel interference, then personnel can be identified, but device complexity increases and processing efficiency decreases
Solution Approach 1:
The patent replaces the complex mechanical system of deep learning algorithms with a simpler computational approach based on binary image conversion, outline detection, and morphological operations. This substitution maintains the effectiveness of personnel interference removal while significantly reducing algorithmic complexity and improving processing efficiency.
Solution Approach 2:
The patent applies parameter changes by transforming the image into a binary format with simplified pixel values (0 and 255), which fundamentally changes the data representation and enables faster processing. This parameter transformation simplifies the subsequent detection and removal operations compared to working with the original complex image data through deep learning models.
Data Source
AI summary
These disclosures relate to a method for detecting and removing personnel interference while measuring volume of an object. An image of an object is obtained, a background information of the image is removed, and the image is converted into a binary image. An outline of the object is detected from the binary image. A determination is made whether the outline extends towards a boundary of the field of view. If the outline of the object does not extend to the boundary of the field of view, the volume of the object is measured. Otherwise, an extending part is removed from the outline using a morphological method, if removable. If the extending part is removed, the volume of the object is measured based on an outline of the object obtained after the extended part is removed, and a class 1 warning is issued. Otherwise, a class 2 warning is issued and the measurement is ended. This approach improves efficiency of the object volume measurement.