Edge-Guided Multi-Scale Fusion and Importance-Aware Learning for Real-Time Semantic Segmentation in Waterborne Navigation

• Autorzy: Chen Li-Jia , Zou Jiamin , Huang Yao , Zhou Yang , Hao Guozhu , Zhang Yi

Identyfikatory

DOI

10.12716/1001.19.02.30

• Języki publikacji: EN

Abstrakty

EN

Effective multi-scale feature representation and focused attention on critical objects are essential for accurate perception of waterborne navigation scenes. To address the insufficient exploitation of multi-scale information in existing methods that leads to imprecise segmentation, this study proposes a real-time semantic segmentation method for waterborne navigation scenes through multi-scale information enhancement and importance-weighted optimization. First, DDRNet-23-slim is selected as the backbone network for feature extraction. An edge-guided branch is embedded into its shallow layers, and a Dynamic Feature Fusion Module (DFFM) is constructed by integrating a lightweight hybrid attention mechanism, effectively enhancing multi-scale feature interaction capabilities. Second, the loss function is improved using an importance-weighted strategy to prioritize critical objects during training. Finally, a parameter-free attention mechanism is introduced in the upsampling stage, maintaining real-time performance while ensuring segmentation stability for key objects under complex background interference. Evaluations on the On_Water and Seaships datasets demonstrate that the proposed method achieves mIoU scores of 83.1% and 73.2%, respectively, with ship segmentation accuracy reaching 88.2% on On_Water. The inference speed attains 69.1 FPS, outperforming mainstream real-time segmentation models (e.g., DDRNet, STDC) in balancing accuracy and efficiency. Notably, it exhibits stronger robustness in complex inland river scenarios with dense shore structures and numerous small targets.

Słowa kluczowe

EN

collision avoidance; computer vision; Artificial Intelligence; AI; navigation systems; autonomous ships; semantic segmentation; obstacle detection; water traffic management

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2025
• Tom: Vol. 19 No. 2
• Strony: 579--587
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Chen Li-Jia

• Wuhan University of Technology, Wuhan, China

autor

Zou Jiamin

• Wuhan University of Technology, Wuhan, China

autor

Huang Yao

• Wuhan Institute of Shipbuilding Technology, Wuhan, China

autor

Zhou Yang

• Wuhan University of Technology, Wuhan, China

autor

Hao Guozhu

• Wuhan University of Technology, Wuhan, China

autor

Zhang Yi

• Hubei University of Chinese Medicine, Wuhan, China

Bibliografia

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Uwagi

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