Liquid level detection method

1. A liquid level detection method comprising:

(a) capturing an image of a liquid surface, a structural surface, and graduation markings provided on the structural surface using an image-capturing device to thereby obtain an initial image;

(b) processing the initial image so as to generate a processed image, and obtaining a level reference value of the liquid surface from the processed image, the level reference value representing a height of the liquid surface in terms of inherent characteristics of the processed image; and

(c) calculating a liquid level of the liquid surface based on a relative proportional relation among the level reference value, an over all height of the processed image in terms of the inherent characteristics of the processed image, and dimensions of any one of the initial and processed images relative to the graduation markings.

2. The liquid level detection method of claim 1 , further comprising:

(d) determining an angle between an optical axis of a lens of the image-capturing device and the liquid surface, and correcting the liquid level of the liquid surface calculated in step (c) based on the angle.

3. The liquid level detection method of claim 2 , wherein, in step (d), the correction of the liquid level is performed using a trigonometric function with respect to the angle between the optical axis of the lens of the image-capturing device and the liquid surface.

4. The liquid level detection method of claim 1 , wherein the dimensions of any one of the initial and processed images relative to the graduation markings are given values, and are values of the graduation markings corresponding respectively to upper and lower boundaries of any one of the initial and processed images.

5. The liquid level detection method of claim 4 , wherein, in step (c), the relative proportional relation used to calculate the liquid level of the liquid surface involves interpolation using the level reference value, the overall height of the processed image, and the dimensions of any one of the initial and processed images relative to the graduation markings.

6. The liquid level detection method of claim 5 , wherein the overall height of the processed image is a number of pixels along a vertical line between the upper and lower boundaries of the processed image, and the level reference value is a number of pixels along the vertical line from the lower boundary of the processed image to a representation of the liquid surface in the processed image.

7. The liquid level detection method of claim 6 , wherein, in step (c), the liquid level of the liquid surface is calculated according to the following formula:

x+a (y−x)

where (x) is the value of the graduation marking corresponding to the lower boundary of any one of the initial and processed images, (y) is the value of the graduation marking corresponding to the upper boundary of any one of the initial and processed images, and (a) is a ratio of the level reference value to the overall height of the processed image.

8. The liquid level detection method of claim 1 , wherein said processing the initial image so as to generate the processed image of step (b) includes:

(b1) performing noise removal processing with respect to the initial image so as to obtain a noise-removed image to be processed;

(b2) performing grayscale transformation processing with respect to the noise-removed image to be processed so as to obtain a grayscale image;

(b3) performing binarization processing with respect to the grayscale image so as to obtain a binarized image;

(b4) performing edge detection processing with respect to the binarized image so as to obtain an edge-detected image; and

(b5) performing straight line detection processing with respect to the edge-detected image so as to obtain the processed image and a liquid surface line, and converting the liquid surface line into the level reference value.

9. The liquid level detection method of claim 8 , wherein, in step (b1), the noise removal processing involves performing integration with respect to the initial image using a smooth filter so as to remove noise.

10. The liquid level detection method of claim 8 , wherein, in step (b2), the grayscale transformation processing involves transforming the noise-removed image to be processed into an image in a grayscale format to obtain the grayscale image.

11. The liquid level detection method of claim 8 , wherein, in step (b3), the binarization processing involves setting each pixel in the grayscale image that has a pixel value lower than a threshold value to a first color, and setting each pixel in the grayscale image that has a pixel value not lower than the threshold value to a second color.

12. The liquid level detection method of claim 11 , wherein, in step (b3), the first color is black and the second color is white, and before generation of the binarized image, histogram frequency distribution processing is conducted.

13. The liquid level detection method of claim 8 , wherein, in step (b4), the edge detection processing involves using a derivative filter to sharpen the binarized image.

14. The liquid level detection method of claim 8 , wherein, in step (b5), the straight line detection processing involves removing noise of surplus line segments that do not form a straight line in the edge-detected image.

15. The liquid level detection method of claim 14 , wherein, in step (b5), the straight line detection processing involves utilization of the Hough transform technique to remove the noise of surplus line segments that do not form a straight line in the edge-detected image.

16. The liquid level detection method of claim 8 , wherein the captured initial image is in a digital signal format.

17. The liquid level detection method of claim 1 , wherein the structural surface is a vertically extending surface that intersects the liquid surface.