unified partial configuration model framework for fast partially occluded object detection in high-resolution remote sensing images

Clicks: 153
ID: 195687
2018
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Abstract
Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10× and 2.5× for airplane and ship, and a detection speedup of maximal 7.2×, 4.1× and 2.5× on three test sets, respectively.
Reference Key
qiu2018remoteunified Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Shaohua Qiu;Gongjian Wen;Jia Liu;Zhipeng Deng;Yaxiang Fan
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10030464
URL
http://www.mdpi.com/2072-4292/10/3/464
Keywords Keywords not found

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