Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
AbstractThe PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named ‘random rotation’ during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large margin on detecting small remote sensing objects. View Full-Text
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Ren, Y.; Zhu, C.; Xiao, S. Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN. Appl. Sci. 2018, 8, 813.
Ren Y, Zhu C, Xiao S. Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN. Applied Sciences. 2018; 8(5):813.Chicago/Turabian Style
Ren, Yun; Zhu, Changren; Xiao, Shunping. 2018. "Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN." Appl. Sci. 8, no. 5: 813.
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