A novel detector for floating objects based on continual unsupervised domain adaptation strategy

被引：0

作者：

Chen R. ^{[1
]}

Peng Y. ^{[1
]}

Li Z. ^{[2
]}

机构：

[1] Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian

[2] Institute of Systems Engineering, Dalian University of Technology, Dalian

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 11期

关键词：

continual learning; deep learning; floating materials; object detection; unsupervised domain adaptation;

D O I：

10.12305/j.issn.1001-506X.2023.11.04

中图分类号：

学科分类号：

摘要：

For small-scale targets and domain transfer problems, a method based on a continuous unsupervised domain adaptation strategy is proposed. By removing low-resolution feature maps and enhancing high-resolution feature maps, the method improves the ability of small-scale floaters to extract features. This study proposes a continuous unsupervised domain adaptation method that integrates unsupervised domain adaptation, buffering, and sample replay to reduce the constantly varying domain transfer variance in application scenarios. Meanwhile, this study combines the improved detection network with continual unsupervised domain adaption to improve model detection precision and generalization capabilities. Through the experimental verification on the data set of the floating targets, compared with the mainstream methods, the detection accuracy of the proposed method reaches 82. 2 %, the detection speed can reach 68. 5 f/s, the computation amount of floating-point numbers reaches 3. 3 billion, and the size of the model reaches 25. 3 MB. This study extends the application of object detection in water surface vision. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：3391 / 3401

页数：10

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