實時語義分割--ICNet for Real-Time Semantic Segmentation on High-Resolution Images
github代碼:https://github.com/hszhao/ICNet 語義分割算法精度和速度對比: 由圖可以看出,ResNet38,PSPNet,DUC精度雖然較高,但是無法速度相對較慢,無法達到實時,ENet速度較快,但精度較低,而本文算法既可以達到實時,精度也相對較高. Speed Analysis PSPNet50的處理不同大小的輸入圖像所需時間: 圖中,stage5的卷積核個數
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相關文章
- 1. 實時語義分割--ICNet for Real-Time Semantic Segmentation on High-Resolution Images
- 2. 語義分割，ICNet for Real-Time Semantic Segmentation
- 3. 【圖像分割論文閱讀】ICNet for Real-Time Semantic Segmentation on High-Resolution Images
- 4. 【語義分割綜述】A Survey On Deep Learning-based Architectures For Semantic Segmentation On 2D Images
- 5. ICNet for Real-Time Semantic Segmentation on High-Resolution Image
- 6. 【論文】ICNet for Real-Time Semantic Segmentation on High-Resolution Images
- 7. 《ICNet for Real-Time Semantic Segmentation on High-Resolution Images》論文筆記
- 8. 語義分割--Global Deconvolutional Networks for Semantic Segmentation
- 9. 語義分割--Understanding Convolution for Semantic Segmentation
- 10. 語義分割--Fully Convolutional DenseNets for Semantic Segmentation