@article{LI2025, 
author = {Yunhong LI and Xiaoshuang WEI and Xueping SU and Limin LI and Gufeng TIAN and Teji HAO and Zhunruo FENG and Shibo LI},
title = {Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion},
year = {2025},
journal = {Journal of Northwest University (Natural Science Edition)},
volume = {55},
number = {2},
pages = {277-285},
keywords = {remote sensing image small target detection, multiscale feature enhancement, Split triage, adaptive mechanism, interactive fusion of detailed features},
url = {https://www.sciopen.com/article/10.16152/j.cnki.xdxbzr.2025-02-005},
doi = {10.16152/j.cnki.xdxbzr.2025-02-005},
abstract = {In order to achieve remote sensing image small object detection task, there are problems such as poor feature extraction and fusion due to the blurring of target detail texture information, and leakage of small targets, a remote sensing objects detection algorithm that leverages multiscale feature enhancement and interactive fusion is proposed. Firstly, the multiscale feature enhancement (MFE) module with cross-layer and multi-branch connection structure is used to enrich and enhance the texture feature information obtained from different gradients by using the Split shunt operation, and at the same time, the lightweight feature phantom module Ghost is introduced to perform the linear transformation of the channel, generating more effective feature detail information flow to enhance attention to local detail feature information in the image. Secondly, the feature interaction fusion (FIF) module is constructed, which introduces a multi-branch serial parallel convolution block and an adaptive mechanism pooling block to interact with the channel semantic information and spatial transformation of the input features, capture the global context information, and accurately locate the key position of the small targets. information, enhance the correlation between feature information, and achieve multi-dimensional interactive fusion of fine-grained features. The proposed algorithm is validated with DIOR, which is a remote sensing dataset. The optimized network model achieves a mean accuracy precision of 87.6%, which is higher than the other seven excellent algorithms including NPMMR-Det, YOLOv7, and YOLOv5. The improved small target detection algorithm for remote sensing images achieves better detection accuracy.}
}