TY - GEN
T1 - Enhanced Single-Shot Detector for Small Object Detection in Remote Sensing Images
AU - Shamsolmoali, Pourya
AU - Zareapoor, Masoumeh
AU - Yang, Jie
AU - Granger, Eric
AU - Chanussot, Jocelyn
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Object detection
KW - feature pyramid network
KW - remote sensing images
UR - https://www.scopus.com/pages/publications/85140362888
U2 - 10.1109/IGARSS46834.2022.9884546
DO - 10.1109/IGARSS46834.2022.9884546
M3 - 会议稿件
AN - SCOPUS:85140362888
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1716
EP - 1719
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -