Detection of small and large objects with different image resolutions and distances

[BojanAndonovski71]

Hi

Im trying to detect multiple small and large objects (workers on the construction field can have small size if image was taken from far distance or large if it was closed). I include two sample images just to show you the size of the objects trying to detect and how it looks like in the image.

Here is my data Set:

1 class
923 images for training and 116 images for validation
Images resolution are different as were taken from different cameras such as: 2112 x 1584, 1920x1080, 3264x1836
So workers are dynamic objects that can change the locations/positions (as they can stand/seat and work or can move)
Training images are not same , changes in objects dimensions depending from which distance were taken.
Detection images are almost the same as training images.

So I have some questions : 1- Which cfg file you recommend to use for training? 2- What is the recommended width and height to use for training and detection? 3- How to calculate correct anchors for my data set? 4- What is the num_of_clusters , final_width and final_height and how to calculate it for my data set?

Here the detected clusters when running ./darknet detector calc_anchors /home/darknet/build/darknet/x64/data/workers.data -num_of_clusters 9 -width 640 -height 640 -show

num_of_clusters = 9, width = 640, height = 640
read labels from 921 images
loaded image: 921 box: 5858
all loaded.

calculating k-means++ ...

iterations = 63

avg IoU = 75.19 %

Saving anchors to the file: anchors.txt
anchors = 8, 25, 16, 53, 21, 88, 31, 66, 31,125, 50,106, 47,199, 86,160, 72,294

Also I can not get better maP of 90 % even after 40 000 iterations. And the avarege loss is not getting better than 1 as well . Any help?

[Veccoy]

Hello !

I have the same problem for small defects detection on large surfaces. Have you managed to solve the problem you had with small workers ?