How can I implement semantic segmentation using Keras

Question

Can i know How can I implement semantic segmentation using Keras?

Answer 1

To implement semantic segmentation using Keras, use an encoder-decoder architecture like U-Net, DeepLabV3+, or SegNet, train on pixel-wise labeled data, and apply techniques like data augmentation and dice loss for better accuracy.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Uses U-Net Architecture:

  • Encoder (downsampling) extracts features, and decoder (upsampling) reconstructs the segmentation mask.

• Skip Connections for Fine-Grained Details:

  • Prevents loss of spatial information by concatenating encoder and decoder features.

• Pixel-Wise Classification with Sigmoid Activation:

  • Each pixel is assigned a segmentation class label.

• Custom Loss Functions (Dice Loss or Cross-Entropy):

  • Can be optimized using binary/categorical cross-entropy or Dice coefficient loss for better segmentation accuracy.

• Supports Multi-Class Segmentation (num_classes > 1):

  • Modify the output activation to "softmax" for multi-class segmentation.

Hence, semantic segmentation using U-Net in Keras enables pixel-wise classification, leveraging skip connections and loss functions like Dice loss for precise object segmentation.