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I am trying to detect the circle inside traffic light, and I am able to detect only 1 out of the 2 circle, and the size of the circle which i am getting seems to be too big

Input Image: https://i.imgur.com/VkNDt2B.png

Output image: https://i.imgur.com/BBq5tE0.png

int main()

 Mat src, gray;
 src = imread("C:/test_image2.png", 1);
 resize(src, src, Size(640, 480));

 cvtColor(src, gray, CV_BGR2GRAY);

 // Reduce the noise so we avoid false circle detection
 GaussianBlur(gray, gray, Size(9, 9), 2, 2);

 vector<Vec3f> circles;

 // Apply the Hough Transform to find the circles
 HoughCircles(gray, circles, CV_HOUGH_GRADIENT, 1, 60, 200, 20, 0, 35);

 // Draw the circles detected
 for (size_t i = 0; i < circles.size(); i++)
 
 Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
 int radius = cvRound(circles[i][2]);
 circle(src, center, 3, Scalar(0, 255, 0), -1, 8, 0);// circle center 
 circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);// circle outline
 cout << "center : " << center << "nradius : " << radius << endl;
 

 // Show your results
 namedWindow("Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE);
 imshow("Hough Circle Transform Demo", src);

 waitKey(0);
 return 0;

HoughCircles works best if you know in advance the approx size of the circles you're looking for. I suggest you give a better value for min_radius and max_radius parameters.
In any case, you need to play with param1 and param2 parameters. If circles are not perfect circles you can try to lower the image resolution using the dp parameter (f.ex. with dp = 2 the image is downscaled to half its resolution).
Basically: play with param1 and param2 until your circles are detected, no matter if other circles are detected. Use this result to find out what radius your circles are, then fix the min and max radius to remove most circles you don't want and finally play again with param1 and param2 until only your circles are left.

this is a pretty huge image
try cropping to the traffic light part first ( to get something to begin with ) and then by trying different combinations of min_distance and param_1,param_2 parameter try getting most circles ( even the wrong ones ) detected. find out what values get the most circles and what combination gets least ( or no ) circles and then fine tune the parameters to get lesser circles detected and finally find the perfect combination