Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
一、RCNN系列對比 RCNN是在原圖上做region proposal,即在低級特徵上做region proposal，產生的推薦區域多，導致計算量大，而且因爲低級特徵對於圖像分類辨識度不高，所以低級特徵的輸入使得最終的分類精度低，也就是說，它既慢又不準 Faster RCNN是在高級特徵圖上做region proposal，高級特徵圖對圖像分類具有很好的辨識度，從而讓分類和邊框迴歸的精度更高
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- 4. 論文翻譯：Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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- 8. [論文學習]《Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 》
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- 10. (41)[NIPS15] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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相關文章
- 1. 【Faster RCNN】《Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks》
- 2. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- 3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks論文筆記
- 4. 論文翻譯：Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- 5. 論文筆記：Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- 6. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks （faster rcnn 論文翻譯）
- 7. Faster-RCNN（Region Proposal Networks）
- 8. [論文學習]《Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 》
- 9. 【論文翻譯】Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- 10. (41)[NIPS15] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks