Our task is to do a special object detection: In the traditional case, the neural network will output some rectangle bounding boxes. But in our case, the network should output many nearly-vertical lines. The lines will cut the image into many pieces, each piece will be one object. (Our object distribution is very special, and such lines perfectly exist.)

EDIT - My naive idea: Let the input image be 512x512x3. The output will be 1x64x3. The 3 channels are (a) the y coordinate of the line when x=256 (b) the angle of line (c) the confidence that the line exists. Note that the 64x1 means this output is very fat. Actually, it is just like YOLO/SSD, but in our case each row predicts one line, instead of each cell predicting one box. However, I really have no idea how to implement the inner network structure... I would appreciate for any ideas!

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    $\begingroup$ Your problem is interesting, but you should try to ask a more specific question. For example, "can this be done in Yolo? If not, how could I achieve this?" $\endgroup$ – nbro Apr 2 '20 at 12:59
  • $\begingroup$ @nbro Thanks for the suggestions! Edited and add my naive thoughts, looking forward to hearing from you :) $\endgroup$ – ch271828n Apr 2 '20 at 13:40

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