[1]QU Haicheng,LI Ruike,WANG Meng,et al.Ship detection in remote sensing images via feature reuse and dilated convolution[J].CAAI Transactions on Intelligent Systems,2024,19(5):1298-1308.[doi:10.11992/tis.202304002]
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Ship detection in remote sensing images via feature reuse and dilated convolution

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1298-1308 Column: 人工智能院长论坛 Public date: 2024-09-05
Title:
Ship detection in remote sensing images via feature reuse and dilated convolution
Author(s):
QU Haicheng;  LI Ruike;  WANG Meng;  SHAN Yimeng
College of Software, Liaoning Technical University, Huludao 125105, China
Keywords:
remote sensing image; ship detection; feature reuse; dilated convolution; split attention; group convolution; feature pyramid; deformable convolution
CLC:
TP751；TN911.73
DOI:
10.11992/tis.202304002
Abstract:
In optical remote sensing images, ship targets in ports are often densely grouped and obstructed by the surrounding environment, such as containers and vehicles. To further improve the accuracy and generalization performance of existing ship target detection algorithms, this study proposes a remote sensing image ship detection algorithm based on feature reuse and dilated convolution. First, a residual block based on grouped convolution and split attention is constructed to extract features, with deformable convolution embedded to better handle ship scale variations. Afterward, a multiscale receptive field module is designed to reduce information loss by parallel extraction and fusion of multiscale features. Finally, a bottom-up feature reuse aggregation path is developed based on the original feature pyramid to enhance feature representation. Experiments were conducted on the large-scale remote sensing dataset, DOTA and the ship dataset, HRSC2016. The results show that the proposed method effectively alleviates the issues of missing and false detections of ship targets, leading to increased accuracy in ship target detection in remote sensing images.
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