47 lines
2.7 KiB
TeX
47 lines
2.7 KiB
TeX
\section{Ball detection}
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The very first task that needed to be accomplished was to detect the ball,
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which is uniformly red-colored and measures about 6 cm in diameter. We decided
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to use a popular algorithm based on color segmentation. The idea behind this
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algorithm is to find the biggest red area in the image and assume that this is
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the ball. First, the desired color needs to be defined as an interval of HSV
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(Hue-Saturation-Value) values. After that, the image itself needs to be
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transformed into HSV colorspace, so that the regions of interest can be
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extracted into a \textit{binary mask}. The contours of the regions can then be
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identified in a mask, and the areas of the regions can be calculated using the
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routines from the OpenCV library. The center and the radius of the region with
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the largest area are then determined and are assumed to be the center and the
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radius of the ball.
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It is often recommended to eliminate the noise in the binary mask by applying a
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sequence of \textit{erosions} and \textit{dilations}, but we found, that for
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the task of finding the \textit{biggest} area the noise doesn't present a
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problem, whereas performing erosions may completely delete the image of the
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ball, if it is relatively far from the robot and the camera resolution is low.
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For this reason it was decided not to process the binary mask with erosions and
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dilations, which allowed us to detect the ball even over long distances.
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The advantages of the presented algorithm are its speed and simplicity. The
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major downside is that the careful color calibration is required for the
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algorithm to function properly. If the HSV interval of the targeted color is
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too narrow, then the algorithm might miss the ball; if the interval is too
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wide, then other big red-shaded objects in the camera image will be detected as
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the ball. A possible approach to alleviate these issues to a certain degree
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will be presented further in this chapter. To conclude, we found this algorithm
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to be robust enough for our purposes, if the sensible color calibration was
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provided.
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\section{Goal detection}
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The goal detection presented itself as a more difficult task. The color of the
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goal is white, and there are generally many white areas in the image from the
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robot camera, which have area larger than that of the image of the goal, for
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example the white field lines and the big white wall in the room with the
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field. To deal with the multitude of the possible goal candidates, we
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propose the following algorithm.
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First, all contours around white areas are extracted by using a procedure
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similar to that described in the section on ball detection. Only $N$ contours
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with the largest areas are considered further (in our experiments it was
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empirically determined that $N=5$ provides good results).
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