High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n

Kun Lan, Xiaoliang Jiang (), Xiaokang Ding, Huan Lin and Sixian Chan
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Kun Lan: College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
Xiaoliang Jiang: College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
Xiaokang Ding: College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
Huan Lin: College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
Sixian Chan: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Mathematics, 2024, vol. 12, issue 7, 1-14

Abstract: With the development of the intelligent vision industry, ship detection and identification technology has gradually become a research hotspot in the field of marine insurance and port logistics. However, due to the interference of rain, haze, waves, light, and other bad weather, the robustness and effectiveness of existing detection algorithms remain a continuous challenge. For this reason, an improved YOLOv8n algorithm is proposed for the detection of ship targets under unforeseen environmental conditions. In the proposed method, the efficient multi-scale attention module (C2f_EMAM) is introduced to integrate the context information of different scales so that the convolutional neural network can generate better pixel-level attention to high-level feature maps. In addition, a fully-concatenate bi-directional feature pyramid network (Concatenate_FBiFPN) is adopted to replace the simple superposition/addition of feature map, which can better solve the problem of feature propagation and information flow in target detection. An improved spatial pyramid pooling fast structure (SPPF2+1) is also designed to emphasize low-level pooling features and reduce the pooling depth to accommodate the information characteristics of the ship. A comparison experiment was conducted between other mainstream methods and our proposed algorithm. Results showed that our proposed algorithm outperformed other models by achieving 99.4% of accuracy, 98.2% of precision, 98.5% of recall, 99.1% of mAP@.5, and 85.4% of mAP@.5:.95 on the SeaShips dataset.

Keywords: ship detection; YOLOv8; multi-scale attention; bi-directional; spatial pyramid pooling fast (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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