An efficient model for small object detection in the maritime environment

Shao, Zeyuan; Yin, Yong; Lyu, Hongguang; Guedes Soares, C.; Cheng, Tao; Jing, Qianfeng; Yang, Zhilin

doi:10.1016/j.apor.2024.104194

Applied Ocean Research

2024

DOI: 10.1016/j.apor.2024.104194

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An efficient model for small object detection in the maritime environment

Zeyuan Shao,

Yong Yin,

Hongguang Lyu

et al.

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Cited by 8 publications

References 60 publications

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Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Thomas,

Kuang,

Wang

et al. 2024

Vis Comput

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The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception. Graphical abstract

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Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Thomas,

Kuang,

Wang

et al. 2024

Vis Comput

View full text Add to dashboard Cite

show abstract

YOLO-MMS for aerial object detection model based on hybrid feature extractor and improved multi-scale prediction

Junos,

Khairuddin

2024

Vis Comput

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PLCFishMOT: multiple fish fry tracking utilizing particle filtering and attention mechanism

Tan,

Cheng,

Liu

et al. 2024

Aquacult Int

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An efficient model for small object detection in the maritime environment

Cited by 8 publications

References 60 publications

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

YOLO-MMS for aerial object detection model based on hybrid feature extractor and improved multi-scale prediction

PLCFishMOT: multiple fish fry tracking utilizing particle filtering and attention mechanism

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