Detection with aspect ratio change

[fishman2008]

For instance, the input image has a resolution of 1600900. When it is resized to 416416 or other larger sizes, the detection will almost fail. One solution is to crop overlapped images to perform detection separately and then merge the results. Any better solutions available? Would it be possible to add the aspect ratio change to data augmentation?

[AlexeyAB]

The problem is not in the aspect ratio, but in low resolution. If the 1600 x 900 image has small objects, and if this image is resized to 416 x 416 then even a human will not be able to see these small objects.

The best solution is to set a higher network resolution, but not higher than image/video resolution. You can change random=0, width=736 and height=416 or better set random=0, width=800 and height=800 in the cfg-file and train your model for this aspect ratio. Then for detection use the same width=736 and height=416 in cfg file.

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During training, when the image have been loaded - the augumentation occurs :

• part of image is randomly cropped with different width and height, so it changes aspect ratio:

  darknet/src/data.c

  Line 704 in 9920410

  image cropped = crop_image(orig, pleft, ptop, swidth, sheight);

  • it depends on jitter=0.2 in cfg-file:

    darknet/cfg/yolo-voc.2.0.cfg

    Line 234 in 9920410

    jitter=.2

• then this part of image resized to the network size:

  darknet/src/data.c

  Line 709 in 9920410

  image sized = resize_image(cropped, w, h);

• and then randomly changed hue, saturation, exposure, so it changes color and lighting:

  darknet/src/data.c

  Line 711 in 9920410

  random_distort_image(sized, hue, saturation, exposure);

  • it depends on cfg-file:

    darknet/cfg/yolo-voc.2.0.cfg

    Line 10 in 9920410

    saturation = 1.5

  • about HSV (hue, saturation, exposure): https://en.wikipedia.org/wiki/HSL_and_HSV

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But you should know:

1. First of all, the high network resolution is important (the higher - the better). I.e. 800 x 800 will be better than 736 x 416, even if your input image 1600 x 900.
2. And only In second place in importance is the aspect ratio.