期刊
REMOTE SENSING
卷 10, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/rs10030464
关键词
high-resolution remote sensing images; partially occluded object detection; partial configuration model; unified detection framework; part sharing; deformable part-based model
类别
资金
- Program for New Century Excellent Talents in the University of China [NCET-11-0866]
- National Natural Science Foundation of China [41601487]
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 10x and 2.5x for airplane and ship, and a detection speedup of maximal 7.2x, 4.1x and 2.5x on three test sets, respectively.
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