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ISPRS Int. J. Geo-Inf. 2019, 8(1), 49;

Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks

School of Geography, Geomatics and Planning, Jiangsu Normal University, Xu Zhou 221116, Jiangsu, China
State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, Hebei, China
Bureau of Land and Resources of Xuzhou, Xuzhou 221006, Jiangsu, China
Authors to whom correspondence should be addressed.
Received: 28 December 2018 / Revised: 13 January 2019 / Accepted: 16 January 2019 / Published: 19 January 2019
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With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. However, several challenges limit the applications of DCNN in manhole cover object detection using remote sensing imagery: (1) Manhole cover objects often appear at different scales in remotely sensed images and DCNNs’ fixed receptive field cannot match the scale variability of such objects; (2) Manhole cover objects in large-scale remotely-sensed images are relatively small in size and densely packed, while DCNNs have poor localization performance when applied to such objects. To address these problems, we propose an effective method for detecting manhole cover objects in remotely-sensed images. First, we redesign the feature extractor by adopting the visual geometry group (VGG), which can increase the variety of receptive field size. Then, detection is performed using two sub-networks: a multi-scale output network (MON) for manhole cover object-like edge generation from several intermediate layers whose receptive fields match different object scales and a multi-level convolution matching network (M-CMN) for object detection based on fused feature maps, which combines several feature maps that enable small and densely packed manhole cover objects to produce a stronger response. The results show that our method is more accurate than existing methods at detecting manhole covers in remotely-sensed images. View Full-Text
Keywords: manhole cover; remote sensing images; object detection; deep convolutional neural networks manhole cover; remote sensing images; object detection; deep convolutional neural networks

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Liu, W.; Cheng, D.; Yin, P.; Yang, M.; Li, E.; Xie, M.; Zhang, L. Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks. ISPRS Int. J. Geo-Inf. 2019, 8, 49.

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