SegNet, introduced in 2015, is a deep learning architecture specifically designed for semantic segmentation. Semantic segmentation is a process of classifying each pixel in an image into a predefined category.

In this article, we are going to explore the architecture of SegNet.

What is SegNet?

SegNet is a deep learning architecture designed for semantic segmentation, where the goal is to classify each pixel in an image into a predefined category. It is an encoder-decoder neural network specifically tailored for pixel-wise image segmentation, making it highly effective for tasks that require detailed and precise segmentation.

SegNet’s primary purpose is to perform semantic segmentation by learning to label each pixel in an image according to its category. This makes SegNet particularly useful in applications such as autonomous driving, medical image analysis, and urban scene understanding, where accurate segmentation is crucial.

SegNet Architecture Detailed Overview

SegNet is a deep learning architecture designed for semantic pixel-wise image segmentation. The architecture includes an encoder network and a corresponding decoder network, followed by a final pixel-wise classification layer. This detailed explanation covers each component of SegNet, comparisons with other architectures, and various decoder variants.

Encoder Network

The encoder network in SegNet is composed of 13 convolutional layers, mirroring the first 13 convolutional layers of the VGG16 network, which was originally designed for object classification. Key points about the encoder network are:

1. Pre-trained Weights: The use of VGG16’s pre-trained weights allows for efficient initialization and faster convergence during training.
2. Convolutional Layers: These layers perform convolution operations to extract features from the input image.
3. Batch Normalization: Each convolutional layer is followed by batch normalization to stabilize and accelerate the training process.
4. ReLU Activation: Rectified Linear Unit (ReLU) activation function is applied element-wise to introduce non-linearity.
5. Max-Pooling: Max-pooling with a 2×2 window and a stride of 2 is used to downsample the feature maps, reducing their spatial resolution by half. This step helps in achieving translation invariance over small spatial shifts.

The max-pooling operation results in a lossy representation of the image, especially in terms of boundary details, which are crucial for segmentation tasks. To mitigate this loss, the locations of the maximum feature values in each pooling window (max-pooling indices) are stored. This information is later used in the decoder network for accurate upsampling.

Decoder Network

The decoder network consists of 13 layers, each corresponding to an encoder layer. The decoding process is designed to upsample the feature maps back to the original image resolution. Key steps in the decoder network are:

1. Upsampling Using Max-Pooling Indices: The stored max-pooling indices are used to upsample the feature maps, creating sparse feature maps. This technique ensures that the spatial locations of features are preserved.
2. Convolution with Trainable Filters: The sparse feature maps are convolved with trainable decoder filters to produce dense feature maps. This step helps in refining the feature maps and improving segmentation accuracy.
3. Batch Normalization: Similar to the encoder, batch normalization is applied to each layer in the decoder network.
4. Soft-Max Classifier: The final output of the decoder network is passed through a multi-class soft-max classifier, which assigns class probabilities to each pixel. The predicted segmentation is obtained by taking the class with the highest probability for each pixel.

Comparison of SegNet with Other Architectures

Compared to DeconvNet:

• SegNet: Uses max-pooling indices for upsampling without learning, followed by convolution with trainable filters.
• DeconvNet: Uses deconvolution (transposed convolution) for upsampling and involves more parameters and computational resources due to fully connected layers.

Compared to U-Net:

• SegNet: Stores only the max-pooling indices, making it more memory efficient.
• U-Net: Transfers entire feature maps from the encoder to the decoder, requiring more memory but preserving more spatial information.
• ng more detailed spatial information. Unlike SegNet, U-Net does not use max-pooling indices.

Decoder Variants

To evaluate the effectiveness of different decoding techniques, several variants of the SegNet and FCN architectures were tested. These variants include:

1. SegNet-Basic: A simplified version of SegNet with 4 encoders and 4 decoders. This variant uses max-pooling indices for upsampling without learning.
2. SegNet-Basic-EncoderAddition: This variant adds the encoder feature maps to the corresponding decoder feature maps after upsampling.
3. SegNet-Basic-SingleChannelDecoder: Uses single-channel decoder filters, significantly reducing the number of trainable parameters and inference time.
4. FCN-Basic: A simplified version of FCN with 4 encoders and 4 decoders, using deconvolution for upsampling.
5. FCN-Basic-NoAddition: This variant of FCN discards the step of adding encoder feature maps to the decoder feature maps.
6. FCN-Basic-NoDimReduction: Does not perform dimensionality reduction on encoder feature maps, retaining the full resolution.

Training Process of SegNet

The SegNet and its variants were trained and evaluated using the CamVid road scenes dataset, consisting of 367 training and 233 testing RGB images. Key aspects of the training process include:

1. Data Preprocessing: Local contrast normalization is applied to the RGB input images to improve training stability.
2. Training Parameters: Stochastic gradient descent (SGD) with a fixed learning rate of 0.1 and momentum of 0.9 is used. The training process continues until the loss converges.
3. Loss Function: Cross-entropy loss is used, summed over all pixels in a mini-batch. Class balancing is applied using median frequency balancing to address the class imbalance in the dataset.

Performance Metrics

The performance of the decoder variants is evaluated using several metrics:

• Global Accuracy (G): Overall pixel-wise accuracy.
• Class Average Accuracy (C): Average accuracy across all classes.
• Mean Intersection over Union (mIoU): A measure of overlap between the predicted and ground truth segments.
• Boundary F1 Score (BF): Evaluates the accuracy of boundary delineation.

Conclusion

SegNet is a highly efficient and effective architecture for image segmentation, leveraging pre-trained weights and innovative upsampling techniques. Its various decoder variants and comparisons with FCN and U-Net highlight its strengths in memory efficiency and segmentation accuracy, making it suitable for practical applications, especially in resource-constrained environments.