Guide to Fully Convolutional Networks for Semantic Segmentation

Introduction

Semantic segmentation, categorizing images pixel-by-pixel into specified groups, is a crucial problem in computer vision. Fully Convolutional Networks (FCNs) were first introduced in a seminal publication by Trevor Darrell, Evan Shelhamer, and Jonathan Long in 2015. This ground-breaking method completely changed the field by providing end-to-end training for semantic segmentation tasks, doing away with the requirement for conventional fully connected layers, and enabling more accurate and efficient pixel-wise classification. Moreover, FCNs have established themselves as a fundamental method in computer vision, greatly enhancing applications like medical imaging, autonomous driving, and scene comprehension.

Overview

1. To present and discuss Fully Convolutional Networks (FCNs) and their significance in semantic segmentation problems.
2. To describe FCNs’ key inventions and architecture, including the encoder-decoder structure and the usage of skip connections.
3. Compare and contrast the three primary FCN variations (FCN-32s, FCN-16s, and FCN-8s) and analyze their benefits and drawbacks.
4. To investigate the influence of FCNs on computer vision and emphasize potential applicability in various fields, including autonomous driving, medical imaging, satellite imagery processing, and augmented reality.

What are FCNs?

Jonathan Long and colleagues introduced the concept of Fully Convolutional Networks (FCNs) in their groundbreaking study “Fully Convolutional Networks for Semantic Segmentation.” Convolutional Neural Networks (CNNs) have successfully classified images; FCNs improve on this success by tailoring CNNs to dense prediction tasks like semantic segmentation.

Also read: Basics of CNN in Deep Learning

The FCN Innovations

1. Finish-to-end Learning: FCNs make it possible to learn semantic segmentation from beginning to finish, doing away with the need for laborious pre- or post-processing procedures.

2. Arbitrary Input Sizes: Due to their completely convolutional architecture, FCNs, in contrast to conventional CNNs, can handle input images of any size.

3. Effective Inference: Compared to patch-based methods, FCNs enable faster inference by utilising the processing power of convolutions.

FCN Architecture

Two primary parts make up the FCN architecture:

Encoder (downsampling path)

Pretrained classification networks (such as VGG and ResNet) are used, but their fully connected layers are eliminated. Hierarchical features are extracted using a sequence of convolutional and pooling layers.

Decoder (Upsampling Path)

It requires feature maps to be upsampled using transposed convolutions or deconvolution. Combines fine-grained spatial information from previous layers with skip connections.

Connectivity Skips in FCNs

Skip connections are an essential component of FCNs. They allow the network to integrate fine-grained, geographical information from shallower layers with coarse, semantic information from deeper layers. This fusion makes producing segmentation maps with greater accuracy and detail possible.

Also read: A Comprehensive Tutorial to learn Convolutional Neural Networks from Scratch

Variants of FCNs

Three variations of FCN were proposed in the original paper:

1. FCN-32s: Upsampling a single stream from the last layer
2. FCN-16s: Upsampling in two streams using a skip connection from pool 4
3. FCN-8s: Skip connections from pool 4 and pool 3 and three-stream upsampling

Comprehensive FCN Variants Comparison Table

Advantages of FCNs

Here are the advantages of FCNs:

1. Preservation of Spatial Information: For precise segmentation, spatial information is maintained by FCNs across the network.
2. Flexibility: No fixed-size inputs are needed; they can be applied to photos of different sizes.
3. Efficiency: The fully convolutional nature of the data facilitates faster inference and efficient computing.
4. Transfer Learning: This method facilitates efficient transfer learning by utilising pretrained categorization networks.

Restrictions and Future Advancements

Although FCNs were a major advancement, they have certain drawbacks:

1. Resolution Loss: Several pooling layers may cause the fine details to be lost.
2. Context Integration: A small receptive field could struggle to integrate with a large context.

Moreover, because of these restrictions, more research has been conducted, and the FCN framework has been improved and built upon by architects like U-Net, DeepLab, and PSPNet.

Significance and Utilisation

FCNs are being used in several fields, such as:

1. Segmenting objects and roads in autonomous driving
2. Organ segmentation and tumor identification in medical imaging
3. Satellite imagery: identifying changes and classifying land use
4. Augmented Reality: Recognising scenes and interacting with objects

Conclusion

Semantic segmentation has dramatically shifted thanks to fully convolutional networks (FCNs). FCNs have opened the door to more precise and instantaneous segmentation systems by facilitating end-to-end learning and effective inference on arbitrary-sized inputs. Even as the field develops, the fundamental ideas behind many cutting-edge segmentation architectures remain those that FCNs introduced.

Also read: Image Classification Using CNN (Convolutional Neural Networks)

Frequently Asked Questions