Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images
This addresses the challenge of detecting small objects in remote sensing images, which is important for applications like surveillance and environmental monitoring, but it appears incremental as it builds on existing single-shot detector methods.
The paper tackled the problem of small-object detection in remote sensing images by proposing an image pyramid single-shot detector (IPSSD) that enhances small-scale features, achieving superior performance compared to state-of-the-art detectors on two public datasets.
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.