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, 49–54, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-49-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 49–54, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-49-2020

  21 Aug 2020

21 Aug 2020

VEHICLE DETECTION IN HIGH RESOLUTION IMAGE BASED ON DEEP LEARNING

H. Gao and X. Li H. Gao and X. Li
  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China

Keywords: Deep Learning, Vehicle Detection, SSD, High Resolution, Convolutional Neural Network

Abstract. Despite its high accuracy and fast speed in object detection, Single Shot Multi-Box Detector (SSD) tends to get undesirable results especially for small targets such as vehicles on high-resolution images. In this paper, we propose a new convolutional neural network based on SSD to detect vehicles on high-resolution images. In the proposed framework, the feature fusion module and detection module are incorporated. In the feature fusion module, feature maps of different scales are integrated into a fusion feature for object detection, which could improve the accuracy effectively. Besides, to prevent the network from overfitting and speed up the training, the batch normalization layer is embedded between the detection layers in the detection module. Some ablation experiments provide strong evidence for the effectiveness of these above structures. On the UCAS-High Resolution Aerial Object Detection Dataset, our network has the ability to achieve the 0.904 AP (average precision) with 0.094 AP higher than SSD512 but similar speed to it.