[1]SHEN Hao,GE Quanbo,WU Gaofeng.Unmanned aerial vehicle-driven sea segmentation based on the shallow and deep features of the backbone[J].CAAI Transactions on Intelligent Systems,2025,20(3):605-620.[doi:10.11992/tis.202405021]
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Unmanned aerial vehicle-driven sea segmentation based on the shallow and deep features of the backbone

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 605-620 Column: 学术论文—机器学习 Public date: 2025-05-05
Title:
Unmanned aerial vehicle-driven sea segmentation based on the shallow and deep features of the backbone
Author(s):
SHEN Hao1;  GE Quanbo2; 3; 4;  WU Gaofeng2
1. School of Computer, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China;
3. Jiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent Systems, Nanjing 210044, China;
4. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing 210044, China
Keywords:
complex maritime scene; semantic segmentation; drone landing; ship target; DeepLabV3+; attention mechanism; deep learning; convolutional neural network
CLC:
TP751
DOI:
10.11992/tis.202405021
Abstract:
To improve the real-time and accurate segmentation of targets during the autonomous landing of UAVs in complex marine environments, studying the impact of backbone and shallow/deep features on the performance of algorithms is crucial. Based on the DeepLabV3+ framework, a Shallow and Deep Features of Backbone (SDFB) algorithm is established for maritime scene segmentation. First, to address the issue of reduced target stability caused by wind-wave disturbances, a feature extraction method is proposed by optimizing the MobileNetV2 structure, and this method resolves the issue of low processing speed of single frame images in the algorithm. Second, to address the issue of numerous deep feature output channels and the uneven distribution of atmospheric turbulence noise, a feature filtering mechanism is proposed by selectively aggregating features using local and global information, thereby eliminating redundant features while solving the high sensitivity issue of the algorithm to environmental noise. Third, to address the issue of uneven lighting reducing the clarity of target boundaries, a parallel contour learning mechanism is established by extracting contour information from shallow spatial dimensions and deep channel dimensions, thereby solving the low-efficiency issue regarding the utilization of contour features. Finally, to address the issue of background occlusion disrupting the integrity of target features, a multi-scale feature fusion mechanism is established through the fusion optimization of strip pooling, and this solves the connection issue of the algorithm to discrete distribution features. Finally, relevant experiments reveal that the LMSC algorithm exhibits higher real-time accuracy than other algorithms and can better adapt to the segmentation requirements of UAVs in maritime scenes.
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