App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

Research on ship target detection of scale adaptive receptive field

Luo Fang1
Li Jiawei1
He Daosen2
1. College of Computer Science & Artificial Intelligence, Wuhan University of Technology, Wuhan Hubei 430063, China
2. Dept. of Supply Chain & Information Management, Hang Seng University of Hong Kong, Hong Kong 999077, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0558

Abstract

The existing ship target detection algorithms mostly rely on optimized improvements based on traditional object detection algorithms, without considering the scale and aspect ratio characteristics of ships, leading to issues such as missed detections and false alarms in multi-scale target detection. To address this, the paper proposes a scale-adaptive receptive field ship detection method SAF-YOLOX built upon the foundation of YOLOXs, Initially, different feature layers extracted by the backbone network are enhanced by constructing a bidirectional feature pyramid, improving feature descriptions at various scales. Simultaneously, an adaptive feature enhancement module is designed to suppress redundant information introduced by the fusion of features at different scales, thereby attenuating background information. During the prediction phase, a multi-branch parallel receptive field detection head is employed, utilizing receptive fields adapted to target sizes and proportions for extracting scale-adaptive feature information. Additionally, a convergence-aware strategy is implemented, dynamically selecting and allocating samples based on the network's convergence state. This strategy ensures improved detection accuracy while maintaining detection speed. Experimental results demonstrate that the proposed method achieves an average detection accuracy of 93.21% on the SeaShips dataset and 92.34% on the MCShips dataset. When compared to traditional YOLOXs, the method exhibits an improvement of 1.01% and 1.09%, respectively. The experimental results confirm that the proposed method, utilizing scale-adaptive receptive fields, can achieve high-precision detection of multi-scale ship targets.

Foundation Support

粤澳科技创新联合资助项目(2021A0505080008)
产学研珠港澳合作项目(ZH22017002200001PWC)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0558
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-01-24] Accepted Paper

Cite This Article

罗芳, 李家威, 何道森. 尺度适应性感受野的船舶目标检测研究 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.10.0558. (Luo Fang, Li Jiawei, He Daosen. Research on ship target detection of scale adaptive receptive field [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.10.0558. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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