【目标检测】R-CNN论文详解（Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation）...
image 该文是目标检测领域的里程碑论文，是fast-rcnn、faster-rcnn等系列的基础。它首次用深度学习CNN的方式进行目标检测的尝试。取得了性能、准确度均大幅高于传统提取特征方式的结果。 一.算法模块 1.Region Proposals区域推荐 在目标检测时，为了定位到目标的具体位置，通常会把图像分成许多子块，然后把子块作为输入，送到目标识别的模型中。分子块的最直接方法叫滑动窗口
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相关文章
- 1. 【目标检测】RCNN (Rich feature hierarchies for accurate object detection and semantic segmentation)
- 2. 目标检测--Rich feature hierarchies for accurate object detection and semantic segmentation(CVPR 2014)
- 3. Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 目标检测系列 --- RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation Te...
- 6. R-CNN（Rich feature hierarchies for accurate object detection and semantic segmentation）论文理解...
- 7. 论文解读：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 8. RCNN学习笔记(1):Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. [RCNN]Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. Rich feature hierarchies for accurate object detection and semantic segmentation (RCNN)笔记