Saturation-Channel Image Analysis for Non-Working Region Detection
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Solution Overview
Problem
Existing image analysis technologies in intelligent devices, such as lawn mowers, fail to accurately recognize non-working regions, leading to reduced efficiency and potential damage from collisions with obstacles.
Innovation Solution
An image analysis method that involves obtaining a saturation channel image, extracting contours, determining target parameter values, and analyzing these values to identify non-working regions using HSV color space segmentation and thresholding techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image analysis technology is used, then the system is simple to implement, but the recognition accuracy of non-working regions is poor
Solution Approach 1:
The patent segments the image analysis process into multiple independent modules: saturation channel processing, contour extraction, parameter determination, and region identification. Each module handles a specific aspect of the analysis, allowing the system to achieve high recognition accuracy through systematic decomposition while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transitions from analyzing only the original RGB image to analyzing the saturation channel image and HSV color space dimensions. By adding the saturation dimension and processing contours at different levels (image level, contour level, pixel level), the system achieves enhanced recognition capability without simply adding more data, but rather transforming the data representation to reveal hidden patterns.
2Reliability
If existing image analysis technology is used, then the processing time is short, but the omission of non-working regions increases
Solution Approach 1:
The patent performs preliminary processing on the saturation channel image before contour extraction and analysis. By pre-processing the saturation channel to enhance contrast and segment working regions from non-working regions early in the pipeline, the system reduces the computational burden on subsequent processing stages while improving the reliability of region identification, thus balancing time and accuracy.
Solution Approach 2:
The patent implements a continuous analysis process that operates at multiple levels simultaneously: image-level saturation thresholding, contour-level parameter analysis, and pixel-level validation. This multi-level continuous processing ensures that no potential non-working region is overlooked while maintaining efficient throughput by parallelizing operations across different analysis levels.
3Measurement precision
If simple contour extraction is used, then the algorithm is easy to implement, but the parameter analysis depth is insufficient
Solution Approach 1:
The patent applies different analysis methods to different parts of the contour based on their local characteristics. For example, it calculates specific parameters (area, perimeter, circularity) for each contour, and further analyzes pixel-level properties within contours that show abnormal characteristics. This localized quality analysis allows precise measurement of contour parameters while avoiding unnecessary complex processing of all image regions uniformly.
Solution Approach 2:
The patent determines multiple target parameters for each contour including area, perimeter, circularity, and color distribution characteristics. By transforming and analyzing these parameters in different representations (raw values, normalized values, statistical distributions), the system achieves precise parameter measurement while managing algorithm complexity through selective parameter calculation based on contour characteristics.
Data Source
AI summary
An image analysis method includes the steps of obtaining a saturation channel image according to an original image; extracting several contours from the saturation channel image; determining target parameter values corresponding to each contour; and determining according to analysis results of the target parameter values corresponding to each contour whether there is a non-working region in the original image. A related image analysis device, computer device, and computer-readable storage medium are also disclosed.