基于 YOLO 和分段任何事物模型自动检测和提取丢失的集装箱

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-09-09 DOI:10.1080/2150704x.2024.2398814

Hongtao Li, Yong Yang, Shengping Wang, Zhigao Chen, Linbang He

引用次数: 0

摘要

人工视觉方法目前常用于破译多波束水柱图像，以获取丢失的船运集装箱的位置和状态。然而，该方法的识别效率 ...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic detection and extraction of lost shipping containers based on YOLO and the segment anything model

The artificial visual method is currently commonly used to decipher multi-beam water column images to obtain the position and state of lost shipping containers. However, the recognition efficiency ...

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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.