Photovoltaic Panel Dust Detection Using Gap Region Segmentation
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Solution Overview
Problem
Existing methods for cleaning photovoltaic panels often mistakenly identify gaps between solar cells as dust, leading to inaccurate determination of regions to be cleaned, which affects the efficiency and accuracy of dust removal.
Innovation Solution
A method and device that determine the region to be cleaned by assigning pixel values in the gap region to a preset value, using feature values and a classification model to differentiate between dust and gap regions, and identifying connected dust pixels to define the cleaning area, thereby avoiding mistaken identification of gaps as dust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dust detection methods are used on photovoltaic panels, then dust regions can be identified, but gap regions between solar cells are mistakenly identified as dust, reducing measurement precision
Solution Approach 1:
The photovoltaic panel image is segmented into different regions (solar cell regions and gap regions) based on structural characteristics. By dividing the image into distinct zones and analyzing each separately, the method avoids misidentifying gap regions as dust, thereby improving both measurement precision and reliability of dust detection.
Solution Approach 2:
Different detection strategies are applied to different regions of the photovoltaic panel. Solar cell regions are analyzed for dust characteristics while gap regions are excluded from dust detection. This localized approach ensures that the detection algorithm adapts to regional variations in the panel structure, preventing false positives in gap areas.
2Measurement precision
If gap regions are excluded from analysis, then dust detection accuracy improves, but the complexity of image processing increases
Solution Approach 1:
The gap regions are identified and excluded from dust detection analysis in advance, before the actual dust detection process begins. This preliminary segmentation step simplifies subsequent processing by reducing the search space and eliminating regions that would otherwise cause false detections, thereby improving accuracy without proportionally increasing overall complexity.
Data Source
AI summary
The present disclosure provides a method and a device for determining a region to be cleaned, a dust cleaning apparatus, a terminal for controlling a cleaning robot, and a storage medium. The method includes: determining, in an initial image of a photovoltaic panel, a gap region associated with a gap between cell pieces; assigning first preset values to pixel values of pixels located in the gap region of the initial image to obtain a first value-assigned image, wherein the first preset values are pixel values of pixels in the initial image that correspond to the cell pieces.