99 lines
6.1 KiB
TeX
99 lines
6.1 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 heuristic 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. Next, the
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\textit{candidate preselection} takes place. During this stage only $N$
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contours with the largest areas are considered further (in our experiments it
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was empirically determined that $N=5$ provides good results). Furthermore, all
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convex contours are rejected, since the goal is a highly non-convex shape.
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After that, a check is performed, how many points are necessary to approximate
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the remaining contours. The motivation behind this is the following: it is
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clearly visible that the goal shape can be perfectly approximated by a line
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with 8 straight segments. On an image from the camera, the approximation is
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almost perfect when using only 6 line segments, and in some degenerate cases
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when the input image is noisy, it might be necessary to use 9 line segments to
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approximate the shape of the goal. Any contour that requires a different number
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of line segments to be approximated is probably not the goal. The preselection
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stage ends here, and the remaining candidates are passed to the scoring
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function.
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The scoring function calculates, how different are the properties of the
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candidates are from the properties, an idealized goal contour is expected to
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have. The evaluation is happening based on two properties. The first property
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is based on the observation, that the area of the goal contour is much smaller
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than the area of its \textit{enclosing convex hull}. The second observation is
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that all points of the goal contour must lie close to the enclosing convex
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hull. The mathematical formulation of the scoring function looks like the
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following \todo{mathematical formulation}:
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The contour, that minimizes the scoring function, while keeping its value under
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a certain threshold is considered the goal. If no contour scores below the
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threshold, then the algorithm assumes that no goal was found. Our tests have
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shown, that when the white color is calibrated correctly, the algorithm can
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detect the goal almost without mistakes, when the goal is present in the image.
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The downside of this algorithm, is that in some cases the field lines might
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appear the same properties, that the goal contour is expected to have,
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therefore the field lines can be mistaken for the goal. We will describe, how
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we dealt with this problem, in the following section.
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\section{Field detection}
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The algorithm for the field detection is very similar to the ball detection
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algorithm, but some concepts introduced in the previous section are also used
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here. This algorithm extracts the biggest green area in the image, finds its
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enclosing convex hull, and assumes everything inside the hull to be the field.
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Such rather simple field detection has two important applications. The first
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one is that the robot should be aware, where the field is, so that it doesn't
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try to walk away from the field. Due to time constraints, we didn't implement
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this part of the behavior. The second application of field detection is the
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improvement of the quality of goal and ball recognition. As was mentioned in
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the section on ball detection, the current algorithm might get confused, if
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there are any red objects in the robot's field of view. However, there
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shouldn't be any red objects on the field, except the ball itself. So, if
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everything that's not on the field is ignored, when trying to detect the ball,
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the probability of identifying a wrong object decreases. On the other hand, the
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problem with the goal detection algorithm was that it could be distracted by
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the field lines. So, if everything on the field is ignored for goal
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recognition, then the accuracy can be improved.
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