The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 125–130, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-125-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 125–130, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-125-2020

  21 Aug 2020

21 Aug 2020

OBJECT DETECTION WITH THE HIGH-FREQUENCY CHANGE OF OBJECTS CLASSES

L. Lou, S. Zhang, and S. Zhang L. Lou et al.
  • College of Surveying and Geo-Informatics, TONGJI University, Siping Road, Shanghai, 200092, China

Keywords: Object Detection, Classes Change, Two Stage Detection, Datasets Production, PVA-Net, GoogLeNet

Abstract. 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.