Feb 16, 2019 · The classifier and detector are mutually reinforced with end-to-end training, which further speed up the process and avoid false alarms.

Effectiveness of R2-CNN is validated on hundreds of GF-1 images and GF-2 images that are 18 000× 18 192 pixels,. 2.0-m resolution, and 27620 × 29200 pixels, 0.8 ...

A unified and self-reinforced network called remote sensing region-based convolutional neural network composing of backbone Tiny-Net, intermediate global ...

However, detecting tiny objects in large-scale remote sensing images still remains challenging. Firstly, the extreme large input size makes existing object ...

Pang et al. [11] proposed a unified and self-reinforced convolutional neural network under the end-to-end training framework called R 2 -CNN, ...

YOLO Algorithm for Object Detection Implementation using Python · Object detection & Tracking Deep learning YOLO Detector - Own data · Build your OBJECT DETECTION ...

R2-CNN: Fast Tiny Object Detection in Large-scale Remote Sensing Images [Paper]. Jiangmiao Pang, Cong Li, Jianping Shi, Zhihai Xu, Huajun Feng TGRS 2019.

$\mathcal{R}^2$ -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images ; Journal: IEEE Transactions on Geoscience and Remote Sensing, 2019, № 8, p ...

Missing: Tiny | Show results with:Tiny

To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network (R2-CNN), composing ...