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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-archives-XLIII-B3-2020-125-2020</article-id>
<title-group>
<article-title>OBJECT DETECTION WITH THE HIGH-FREQUENCY CHANGE OF OBJECTS CLASSES</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Lou</surname>
<given-names>L.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhang</surname>
<given-names>S.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhang</surname>
<given-names>S.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>College of Surveying and Geo-Informatics, TONGJI University, Siping Road, Shanghai, 200092, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>21</day>
<month>08</month>
<year>2020</year>
</pub-date>
<volume>XLIII-B3-2020</volume>
<fpage>125</fpage>
<lpage>130</lpage>
<permissions>
<copyright-statement>Copyright: © 2020 L. Lou et al.</copyright-statement>
<copyright-year>2020</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLIII-B3-2020-125-2020.html">This article is available from https://isprs-archives.copernicus.org/articles/isprs-archives-XLIII-B3-2020-125-2020.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLIII-B3-2020-125-2020.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/isprs-archives-XLIII-B3-2020-125-2020.pdf</self-uri>
<abstract>
<p>With the development of deep learning, object detection has a significantly improvement. But most of algorithms only focus on the detection accuracy and speed, they do not consider the difficulty of making training datasets and the time consumption of training detection models, which will have a bad influence on the performance of detection model when the class of objects change in high frequency. This paper proposes a method named double network detection (DN detection), it can improve the efficiency of making training datasets and shorten the time of training model. At the same time, the experiment shows that the DN detection have a good performance in accuracy and speed.</p>
</abstract>
<counts><page-count count="6"/></counts>
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