Manhole cover object detection in remote sensing imagery with deep convolutional neural networks

doi:10.13474/j.cnki.11-2246.2019.0256

Abstract

Abstract: Urban component survey is an important task in the development of digital city management. However, the manhole cover information acquisition still has shortcomings such as low efficiency of manual surveying and high leakage rate. To address these problems, this paper proposes an effective method for detecting manhole cover objects in remote sensing images. We redesign the feature extractor by adopting VGG (visual geometry group) and HED (holistically-nested edge detection) side-output module, which can increase the variety of receptive field size. Then, the detection is performed by a multi-level convolution matching network for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed manhole cover objects to produce stronger response. The results show that the proposed method is more accurate than existing methods for detecting manhole cover in remote sensing images.

Key words: manhole cover, remote sensing images, object detection, deep convolutional neural networks, end to end

CLC Number:

P237

YANG Mengyuan, LIU Wei, YIN Pengcheng, XIE Meng. Manhole cover object detection in remote sensing imagery with deep convolutional neural networks[J]. 测绘通报, 2019, 0(8): 78-81,87.

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