【目標檢測】R-CNN論文詳解（Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation）...
image 該文是目標檢測領域的里程碑論文，是fast-rcnn、faster-rcnn等系列的基礎。它首次用深度學習CNN的方式進行目標檢測的嘗試。取得了性能、準確度均大幅高於傳統提取特徵方式的結果。 一.算法模塊 1.Region Proposals區域推薦 在目標檢測時，爲了定位到目標的具體位置，通常會把圖像分成許多子塊，然後把子塊作爲輸入，送到目標識別的模型中。分子塊的最直接方法叫滑動窗口
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相關文章
- 1. 【目標檢測】RCNN (Rich feature hierarchies for accurate object detection and semantic segmentation)
- 2. 目標檢測--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. 目標檢測系列 --- 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）論文理解...
- 7. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 8. RCNN學習筆記(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)筆記