Evaluation of performance fruit detection algorithm and training the algorithm

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S. on 10 Jan 2022
Answered: yanqi liu on 11 Jan 2022
I am trying to find a way to evaluate the performance of my fruit detection algorithm. The detection of the fruit is currently done with the circular Hough transform. How can I compare this result with the original RGB image and therefore check if the detected fruit are a true positive or a false positive (to calculate the accuracy)? And how is it possible to train the algorithm?
I have added the original RGB image, the black-white image after preprocessing and the parameters of the CHT algorithm.
[centers,radii, metric] = imfindcircles(BW_Canny,[Rmin Rmax],'Sensitivity',0.95,'EdgeThreshold',0.15); % 0.95 0.15
% d = imdistline; %To check manually the approximate radius of the fruit in the image
viscircles(centers, radii,'EdgeColor','b');
For example for image 41, there are 3 true positives and 1 false positive.

Answers (1)

yanqi liu
yanqi liu on 11 Jan 2022
in object detect application,may be use evaluateDetectionPrecision to compare detect location and real location,such as

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