Java Deep Learning Projects

Step 5 – Non-max suppression in case of more than one bounding box

As YOLO predicts more than one bounding box per object, non-max suppression is implemented; it merges all detections that belong to the same object. Therefore, instead of using b^x, b^y, b^h, and b^w, we can use the top-left and bottom-right points. gridWidth and gridHeight are the number of small boxes we split our image into. In our case, this is 13 x 13, where w and h are the original image frame dimensions:

void markObjectWithBoundingBox(Mat file, int gridWidth, int gridHeight, int w, int h, DetectedObject obj) {  
        double[] xy1 = obj.getTopLeftXY(); 
        double[] xy2 = obj.getBottomRightXY(); 
        int predictedClass = obj.getPredictedClass();         int x1 = (int) Math.round(w * xy1[0] / gridWidth); ...