  Small-object detection is a challenging problem. In the last few years, the
convolution neural networks methods have been achieved considerable progress.
However, the current detectors struggle with effective features extraction for
small-scale objects. To address this challenge, we propose image pyramid
single-shot detector (IPSSD). In IPSSD, single-shot detector is adopted
combined with an image pyramid network to extract semantically strong features
for generating candidate regions. The proposed network can enhance the
small-scale features from a feature pyramid network. We evaluated the
performance of the proposed model on two public datasets and the results show
the superior performance of our model compared to the other state-of-the-art
object detectors.
