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Citation:
　Ruolan Zhang,Xingchen Ji,Jinichi Koue,et al.A Comparative Analysis of Deep Learning Approaches for Visual Perception Benchmarks in Ship Navigation[J].Journal of Marine Science and Application,2026,(2):600-616.[doi:10.1007/s11804-025-00703-7]
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A Comparative Analysis of Deep Learning Approaches for Visual Perception Benchmarks in Ship Navigation

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Title:
A Comparative Analysis of Deep Learning Approaches for Visual Perception Benchmarks in Ship Navigation
Author(s):
Ruolan Zhang1;  Xingchen Ji1; 2;  Jinichi Koue2;  Katsutoshi Hirayama2
Affilations:
Author(s):
Ruolan Zhang1;  Xingchen Ji1; 2;  Jinichi Koue2;  Katsutoshi Hirayama2
1. Navigation College, Dalian Maritime University, Dalian, 116000, China;
2. Graduate School of Maritime Sciences, Kobe University, Kobe, 6580022, Japan
Keywords:
Long-range perception|Visual navigation|Dataset|Multiscale detection|Vision benchmark
分类号:
-
DOI:
10.1007/s11804-025-00703-7
Abstract:
The establishment of a reliable benchmark for evaluating model performance is critical for advancing deep learning (DL), including its application in the recognition of the ship navigation environment. Despite the steady progress being made in object detection models across various tasks, maritime navigation presents unique challenges, such as long distances, miscellaneous objects, wide perception scales, and local conditions and features of water areas. Therefore, the improvement of DL approaches for this domain remains a significant challenge. Using a widely applicable offshore image dataset from the ship bridge, we evaluated the performance of the state-of-the-art object detection model from three perspectives: average precision, multiscale feature calculation, and intersection-over-union design, and explored the factors that may affect the model performance evaluation benchmark from the perspective of data quality, scale calculation, feature quantification, and object association. Our experiments have demonstrated that, in the context of object detection tasks within complex water surface traffic scenes, comprehensive model performance evaluation benchmarks are essential. Such benchmarks must incorporate multiple dimensions of the model.

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Memo

Memo:
Received date:2025-1-8;Accepted date:2025-3-7。<br>Foundation item:This work was partially funded by the International Association of Maritime Universities (IAMU) and The Nippon Foundation in Japan. The authors would like to acknowledge the support of the International Association of Maritime Universities (Research Project Number 20240201), The authors also gratefully acknowledge the support from the China Scholarship Council (Grant No. CXXM2209260070).<br>Corresponding author:Katsutoshi Hirayama,E-mail:hirayama@maritime.kobe-u.ac.jp
Last Update: 2026-06-08