Research Article
CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment
Table 1
Network architecture of F-FFM.
| Input | Layers | Type | Channels | Filter | Stride | Output | Size |
| | 1 | Convolutional | 64 | | 1 | ↓1 | | ↓1 | 3 | Convolutional | 64 | | 1 | ↓2 | | ↓2 | | Max pooling | 64 | | 2 | ↓3 | | ↓3 | 1 | Convolutional | 128 | | 1 | ↓4 | | ↓4 | 3 | Convolutional | 128 | | 1 | ↓5 | | ↓5 | | Max pooling | 128 | | 2 | ↓6 | | ↓6 | 1 | Convolutional | 256 | | 1 | ↓7 | | ↓7 | 5 | Convolutional | 256 | | 1 | ↓8 | | ↓8 | | Bilinear interpolation | | | | ↓9 | | ↓9↓4 | | Skip connection | 128 | | 1 | ↓10 | | ↓10 | 1 | Convolutional | 128 | | 1 | ↓11 | | ↓11 | 3 | Convolutional | 128 | | 1 | ↓12 | | ↓12 | | Bilinear interpolation | | | | ↓13 | | ↓13↓1 | | Skip connection | 64 | | 1 | ↓14 | | ↓14 | 1 | Convolutional | 64 | | 1 | ↓15 | | ↓15 | 3 | Convolutional | 64 | | 1 | ↓16 | | ↓16 | | Global residual | 3 | | 1 | | |
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