Multiple detections on same object always happens when we use a sliding window to detect the objects. I come up with an idea that we can
(a) randomly segment the testing image into segments.
(b) do windowed detection on the testing image.
(c) count the number of activated windows/confidence falling in each segments.
(d) output the highest segments

Then we can have both detection and good localization boundary