Abstract
Object detection has been widely researched and a series of algorithms have been proposed. These algorithms also have satisfactory verification results in various public datasets. However, the detection effect of small objects is still not satisfactory. Existing object detection algorithms usually achieves the detection of small objects by learning Multi-scale features, but this brings a large amount of calculation. In this paper, we solve the problem that is difficult to detect with small objects by mapping the non-obvious features of the small object into large object Features with the same feature distribution. Then the super-resolution feature obtained by mapping can be used to significant improve the detection performance in the process of training small objects detection.
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