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I am developing a model of object detection based on fast-rcnn architecture (transfer learning) in tensorflow object detection API. My problem is that created model happens to produce very good results when a searched object is close to camera (99% of frames) however it often fails to recognize objects that are placed in some bigger distance from the camera (50%). What I exactly mean is depicted on the figure: enter image description here

My question is how can I improve the correct recognition on longer distances?

I may also add that my database that I used to train this model already contains a lot of labeled objects that are placed in the distance it has problems with.

[EDIT] I've just realized that my question could be simplified from "distanced objects" to "smaller objects", because from perspective of camera, objects that is further away is simply smaller

Img source: https://pl.123rf.com/photo_73777664_cars-driving-down-city-street-st-petersburg-buildings-architecture.html

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