openCV - object detection: should I use YOLO? If not, which algorithm would more appropriate for this case scenario?

Question

I need to detect object that aren't that much complex: pokestops. Some images of a pokestop:

Screenshot examples:

In the past I have worked with template-matching and a little bit with feature detection and homography but sadly none of my previous work helped me so far in detecting this object.

Snippet of what I've tried so far:

BRISK detectorAndExtractor = BRISK.create();

        final MatOfKeyPoint keyPointsLarge = new MatOfKeyPoint();
        final MatOfKeyPoint keyPointsSmall = new MatOfKeyPoint();

        Mat largeImage = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_COLOR);
        Mat smallImage = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_COLOR);

        detectorAndExtractor.detect(largeImage, keyPointsLarge);
        detectorAndExtractor.detect(smallImage, keyPointsSmall);

        //System.out.println("keyPoints.size() : "+keyPointsLarge.size());
        //System.out.println("keyPoints2.size() : "+keyPointsSmall.size());

        Mat descriptorsLarge = new Mat();
        Mat descriptorsSmall = new Mat();

        detectorAndExtractor.compute(largeImage, keyPointsLarge, descriptorsLarge);
        detectorAndExtractor.compute(smallImage, keyPointsSmall, descriptorsSmall);

        //System.out.println("descriptorsA.size() : "+descriptorsLarge.size());
        //System.out.println("descriptorsB.size() : "+descriptorsSmall.size());

        MatOfDMatch matches = new MatOfDMatch();

        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
        matcher.match(descriptorsLarge, descriptorsSmall, matches);

        //System.out.println("matches.size() : "+matches.size());

        MatOfDMatch matchesFiltered = new MatOfDMatch();

        List<DMatch> matchesList = matches.toList();
        List<DMatch> bestMatches= new ArrayList<DMatch>();

        Double max_dist = 0.0;
        Double min_dist = 100.0;

        for (int i = 0; i < matchesList.size(); i++)
        {
            Double dist = (double) matchesList.get(i).distance;

            if (dist < min_dist && dist != 0)
            {
                min_dist = dist;
            }

            if (dist > max_dist)
            {
                max_dist = dist;
            }

        }

        //System.out.println("max_dist : "+max_dist);
        //System.out.println("min_dist : "+min_dist);

        if(min_dist > 50 )
        {
            //System.out.println("No match found");
            //System.out.println("Just return ");
            return false;
        }

        double threshold = 3 * min_dist;
        double threshold2 = 2 * min_dist;

        if (threshold > 75)
        {
            threshold  = 75;
        }
        else if (threshold2 >= max_dist)
        {
            threshold = min_dist * 1.1;
        }
        else if (threshold >= max_dist)
        {
            threshold = threshold2 * 1.4;
        }

        //System.out.println("Threshold : "+threshold);

        for (int i = 0; i < matchesList.size(); i++)
        {
            Double dist = (double) matchesList.get(i).distance;

            if (dist < threshold)
            {
                bestMatches.add(matches.toList().get(i));
                //System.out.println(String.format(i + " best match added : %s", dist));
            }
        }

        matchesFiltered.fromList(bestMatches);

        //System.out.println("matchesFiltered.size() : " + matchesFiltered.size());


        if(matchesFiltered.rows() >= 1) //TODO >= 4
        {
            System.out.println("match found "+matchesFiltered.rows());
            System.out.println("bestMatches.size: "+bestMatches.size());
            getMatchingPoints(keyPointsLarge,keyPointsSmall,bestMatches);
            return true;
        }
        else
        {
            return false;
        }

I didn't try only Brisk (maybe not the best choice since the object is round), I also tried with SURF following this tutorial: https://docs.opencv.org/3.4/d7/dff/tutorial_feature_homography.html

but I get the following exception:

Exception in thread "main" CvException [org.opencv.core.CvException: cv::Exception: OpenCV(4.7.0) /tmp/opencv-20230625-23894-1o8sdu5/opencv-4.7.0/modules/calib3d/src/fundam.cpp:385: error: (-28:Unknown error code -28) The input arrays should have at least 4 corresponding point sets to calculate Homography in function 'findHomography'

A friend was suggesting to maybe try with a YOLO algorithm. I found a few examples but none with a thorough explanation. What would you suggest? Thank you.