41 lines
2.3 KiB
TeX
41 lines
2.3 KiB
TeX
\section{Ball Detection}
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\label{p sec 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 \cite{ball-detect}. The
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idea behind this algorithm is to find the biggest red area in the image and
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assume that this is the ball. First, the desired color needs to be defined as
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an interval of HSV (Hue-Saturation-Value) \cite{hsv} values. After that, the
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image itself needs to be transformed into HSV colorspace, so that the regions
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of interest can be extracted into a \textit{binary mask}. The contours of the
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regions can then be identified in a mask \cite{contours}, and the areas of the
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regions can be calculated using the routines from the OpenCV library. The
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center and the radius of the region with the largest area are then determined
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and are assumed to be the center and the radius of the ball.
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\begin{figure}[ht]
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\includegraphics[width=\textwidth]{\fig ball-detection}
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\caption[Ball detection]{Ball detection. On the right is the binary mask}
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\label{p figure ball-detection}
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\end{figure}
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It is sometimes recommended \cite{ball-detect} to eliminate the noise in the
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binary mask by applying a sequence of \textit{erosions} and \textit{dilations},
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but we found, that for the task of finding the \textit{biggest} area the noise
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doesn't present a problem, whereas performing erosions may completely delete
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the image of the ball from the mask, if it is relatively far from the robot and
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the camera resolution is low. For this reason it was decided not to process the
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binary mask with erosions and dilations, which allowed us to detect the ball
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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 a 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, the algorithm might miss the ball; if the interval is too
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wide, 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 the section \ref{p sec field detect}. To
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conclude, we found this algorithm to be robust enough for our purposes, if a
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sensible color calibration was provided.
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