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Ship Detection and Recognition Based on Improved YOLOv7

Wei Wu1, Xiulai Li2, Zhuhua Hu1, Xiaozhang Liu3,*

1 School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
2 School of Cyberspace Security, Hainan University, Haikou, 570228, China
3 School of Computer Science and Technology, Hainan University, Haikou, 570228, China

* Corresponding Author: Xiaozhang Liu. Email: email

(This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)

Computers, Materials & Continua 2023, 76(1), 489-498. https://doi.org/10.32604/cmc.2023.039929

Abstract

In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data augmentation techniques, including random rotation, scaling, and cropping, which serve to bolster data diversity and robustness. The distribution of positive and negative samples in the dataset is balanced using random sampling, ensuring a more accurate representation of real-world scenarios. Comprehensive experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches in terms of both detection accuracy and robustness, highlighting the potential of the improved YOLOv7 model for practical applications in the maritime domain.

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Cite This Article

APA Style
Wu, W., Li, X., Hu, Z., Liu, X. (2023). Ship detection and recognition based on improved yolov7. Computers, Materials & Continua, 76(1), 489-498. https://doi.org/10.32604/cmc.2023.039929
Vancouver Style
Wu W, Li X, Hu Z, Liu X. Ship detection and recognition based on improved yolov7. Comput Mater Contin. 2023;76(1):489-498 https://doi.org/10.32604/cmc.2023.039929
IEEE Style
W. Wu, X. Li, Z. Hu, and X. Liu "Ship Detection and Recognition Based on Improved YOLOv7," Comput. Mater. Contin., vol. 76, no. 1, pp. 489-498. 2023. https://doi.org/10.32604/cmc.2023.039929



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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