HSL Ball Detection

I am about to finish the ball detection method, a trick that can be used to detect the soccer ball [hopefully] in every soccer field! I am trying my best to provide this algorithm in a way that no prior configuration is required, however still some parameter is needed to be set before each match. Today, I am going to test it on a real humanoid robot. Maybe later I test it on NAO robots as well!

Photo of the MRL-HSL (Humanoid) Field and Ball.
I'll release push the code on my github (http://github.com/arefmq) as soon as I reach an stable version. Also, I am going to provide a detailed description of how it works!

Comments

Post a Comment

Popular posts from this blog

Stage #1 - Replanting a pineapple

Image
I just have read an article about replanting a pineapple, and guess what; here I am! After reading couple of search queries, I decided to start this process. Let's see what happens! I have removed the rest of the fruit. Also, removed the lower leafs. According to the article [and search queries], I should wait couple of days till the lower part is completely dries. Then I can soak it into water for few weeks and hopefully the roots start to grow! Afterward, I am able to plant it in the clay. The only problem is I need somewhere with really high amount of sunlight! [Which there is none in my room...]. A Pineapple If everything goes well, this cute plant will be my friend for a while. I really enjoy having such a thing on my desk. Please let me know if you have ever tried to replant a pineapple. I am really looking for some experience in this field!
Read more

Brand New Perception

Image
Robot Image - Each region is marked with different number. Our new perception module has just got one more step to be complete and as awesome as ever we imagined. This is a result of real robot, which shows each region as different number. Thanks to my friend Ali!
Read more

YOLO

Image
Yolo, a great library my Colleague (Ashkan) and I have found for detecting objects. YOLO stands for Y ou O nly L ook O nce, and its a deep learning computer vision library [1]. The code base is on C (not C++) and it has GPU support. These features along with the availability of designing new layers make it awesome. But the most significant feature of this library is its speed in detecting objects in real life. The default network on this project is trained to detect variety range of objects. However, the library has a huge disadvantage. It's written in C, which makes it hard to further developments. Although, there is C++ wrapper for it in [2], but the library isn't satisfying. I am still try it to fit it in my program, but after a week no progress has been attained yet... I planed to rewrite the wrapper (and in worst case the library itself) to see if I am able to  1) compile it under c++ and integrate it in my program. and 2) to see if can improve it even faster. The Y
Read more