SS R-CNN: Self-Supervised Learning Improving Mask R-CNN for Ship Detection in Remote Sensing Images
Remote Sensing(2022)
摘要
Due to the cost of acquiring and labeling remote sensing images, only a limited number of images with the target objects are obtained and labeled in some practical applications, which severely limits the generalization capability of typical deep learning networks. Self-supervised learning can learn the inherent feature representations of unlabeled instances and is a promising technique for marine ship detection. In this work, we design a more-way CutPaste self-supervised task to train a feature representation network using clean marine surface images with no ships, based on which a two-stage object detection model using Mask R-CNN is improved to detect marine ships. Experimental results show that with a limited number of labeled remote sensing images, the designed model achieves better detection performance than supervised baseline methods in terms of mAP. Particularly, the detection accuracy for small-sized marine ships is evidently improved.
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关键词
self-supervised learning,marine ship detection,deep learning,remote sensing images,Mask R-CNN
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