Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

Qiu, Shaohua; Wen, Gongjian; Liu, Jia; Deng, Zhipeng; Fan, Yaxiang

doi:10.3390/rs10030464

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Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

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Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China

College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China

College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Authors to whom correspondence should be addressed.

Remote Sens. 2018, 10(3), 464; https://doi.org/10.3390/rs10030464

Received: 15 December 2017 / Revised: 5 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018

(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)

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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. View Full-Text

Keywords: high-resolution remote sensing images; partially occluded object detection; partial configuration model; unified detection framework; part sharing; deformable part-based model

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MDPI and ACS Style

Qiu, S.; Wen, G.; Liu, J.; Deng, Z.; Fan, Y. Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images. Remote Sens. 2018, 10, 464. https://doi.org/10.3390/rs10030464

AMA Style

Qiu S, Wen G, Liu J, Deng Z, Fan Y. Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10(3):464. https://doi.org/10.3390/rs10030464

Chicago/Turabian Style

Qiu, Shaohua, Gongjian Wen, Jia Liu, Zhipeng Deng, and Yaxiang Fan. 2018. "Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images" Remote Sensing 10, no. 3: 464. https://doi.org/10.3390/rs10030464

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Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

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