The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W12, 121–126, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W12-121-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W12, 121–126, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W12-121-2019

  21 Feb 2019

21 Feb 2019

DETECTION OF CITIES VEHICLE FLEET USING YOLO V2 AND AERIAL IMAGES

H. Lechgar, H. Bekkar, and H. Rhinane H. Lechgar et al.
  • Faculté des Sciences Ain Chock, Université Hassan II, Casablanca, Marocco

Keywords: Deep Learning, Convolutional neural network (CNN), Yolo, Dataset, Artificial intelligence (AI), Detection, Cars

Abstract. Recent progress in deep learning methods has shown that key steps in object detection and recognition can be performed with convolutional neural networks (CNN). In this article, we adapt YOLO (You Only Look Once) to a new approach to perform object detection on satellite imagery. This system uses a single convolutional neural network (CNN) to predict classes and bounding boxes. The network looks at the entire image at the time of the training and testing, which greatly enhances the differentiation of the background since the network encodes the essential information for each object. The high speed of this system combined with its ability to detect and classify multiple objects in the same image makes it a compelling argument for use with satellite imagery.