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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2521-2527

Ship object detection based on scale-adaptive receptive field

Luo Fang1
Li Jiawei1
He Daosen2
1. College of Computer Science & Artificial Intelligence, Wuhan University of Technology, Wuhan 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 object 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 detections in multi-scale object detection. To address this, the paper proposed a scale-adaptive receptive field ship detection method(SAF-YOLOX) based on YOLOXs. Firstly, it extracted different feature layers by the backbone network, which were fused by constructing a bidirectional feature pyramid, improving feature representation at various scales. Simultaneously, it designed an adaptive feature enhancement module to suppress redundant information introduced by the fusion of features at different scales, thereby attenuating background information. During the prediction phase, it employed a multi-branch parallel receptive field detection head, utilizing receptive fields adapted to target sizes and proportions for extracting scale-adaptive feature information. Additionally, it implemented a convergence-aware strategy, dynamically selecting and allocating samples based on the network's convergence state. This strategy ensured 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 Printed Article, Vol. 41, 2024 No. 8
Section: Technology of Graphic & Image
Pages: 2521-2527
Serial Number: 1001-3695(2024)08-039-2521-07

Publish History

[2024-01-24] Accepted Paper
[2024-08-05] Printed Article

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方法 [J]. 计算机应用研究, 2024, 41 (8): 2521-2527. (Luo Fang, Li Jiawei, He Daosen. Ship object detection based on scale-adaptive receptive field [J]. Application Research of Computers, 2024, 41 (8): 2521-2527. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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