Abstract: Aiming at the problem that small targets in remote sensing images had some defects, such as less information, easy to be interfered by the background, weak feature expression and so on, which led to the current general target detection algorithm was not ideal in the detection of such small targets. In order to improve the detection ability of small targets in remote sensing images, we proposed an improved algorithm based on RFBNet. The algorithm was based on the framework of RFBNet. Firstly, the self-correcting convolution was used to replace the conventional convolution in the feature extraction network to expand the receptive field and enrich the output, so as to enhance the ability of weak feature extraction. Secondly, a multi-scale feature fusion module was designed to enrich the abstract information of the shallow feature map. Finally, a dense prediction module was designed, and contextual information was integrated in the shallow layer in advance, so that the output of each layer in the final stage contained rich and closely related multi-scale feature information. The proposed algorithm was tested on UCAS_AOD and NWPU VHR-10 datasets, and the average detection accuracy reached 83.4% and 94.8%, respectively. The experimental results show that the proposed algorithm can effectively improve the accuracy of target detection in remote sensing images, and has a significant improvement for the problem of small-scale target detection in remote sensing images.

Key words: target , detection, feature fusion, deep learning, convolutional neural network, remote sensing image