Basically, MemuConvert is an image processing method. The aim is decreasing the labelling process effort in object detection.

Why I Developed The Method ?

I joined a competition with a team and our task was detecting cars with data from a drone. We tried this task in YOLOV3 with its own dataset. However, this was a failure because of the inadequate dataset. Therefore we understood that we had to train an AI from scratch.

The Method

There are two types of data can be got from image, colors ,and shapes. Each has own problems. For instance, shapes algorithms can not find most of the objects such as face or car because they are sum of other elements. We call “car” if it has simply, body and tires. Since most of the objects are inferential results, shape algorithms can not find the result. It finds all of the inference one by one. Sometimes, it also finds artificial expressions such as skyline. Thus, the new method should be based on colors but mostly, images has undesired pixels in desired colors.

A1. Taxi and Building


  1. To get a color from original image for drawing boundary boxes.
  2. To divide an image into rectangles.
  3. Sponge Test
  4. To find dominant color.
  5. To rebuild each rectangles with its own dominant color.
  6. To creating a new image with those rectangles.
  7. To transform image into black and white. (desired color is white and undesired color is black.)
  8. To bulur image for getting rid of noise.
  9. To draw boundary box.

Sponge Test

Sponge test is physical description of what I did in an image. Imagine a sponge with a single red dot. If you squeeze that sponge you will get rid of the redness on the sponge. In conclusion, this physical application gives the dominant color, which gives an opportunity for rebuilding the object with dominant color.

Out put of same image
Flower wanted to be trained but the part of girl’s coat has same color palette.
Kimage = x length
Stage 7 and 8
Result of MemuConvert
Result of color algorithm.
Output of MemuConvert

In Conclusion

The method gives an opportunity to train object detection AI fast because users do not have to change threshold. This makes it a general algorithm to label all of the images with a specific threshold. However, the result of being general algorithm, it does not label all of the images precisely.



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