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.
@article{arxiv.2205.05927,
title = {Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images},
author = {Pourya Shamsolmoali and Masoumeh Zareapoor and Eric Granger and Jocelyn Chanussot and Jie Yang},
journal= {arXiv preprint arXiv:2205.05927},
year = {2022}
}