What is semantic segmentation?

Semantic segmentation is used in many fields, such as self-driving cars, retail image analysis, medical image diagnosis, robotics, and geo-sensing. It is also used for autonomous platforms like self-driving vehicles, drones, robots, etc. Semantic segmentation can identify lane lines and traffic signs. It is also used for facial recognition. As with all these real-life applications, we have to understand what semantic segmentation actually is.

As humans, we can distinctly distinguish multiple entities in any picture. However, this ability is something we have developed unconsciously. This is the reason why computer vision uses semantic segmentation. This technology uses segmentation to distinguish between various entities in any frame. Semantic image segmentation is the ability to classify each pixel in a frame. The aim is to create a dense pixel-wise segmentation map of an image where each pixel is assigned to a specific class or object.

There are now different techniques used in the field of computer vision. These include object detection, instance segmentation, and semantic segmentation. The difference between instance segmentation and semantic segmentation is that both techniques are used to classify pixels. However, in instance segmentation, we give each entity a different label even if they are in the same group. While in semantic segmentation, we only classify the pixels into general groups. 

How it works

Semantic segmentation involves extracting features from an image and categorizing them into separate classes. Without getting into the intricacies, the process involves the following steps.

Data Collection and Annotation

A large dataset of images is collected, and each image is carefully annotated with pixel-level labels indicating the object or class. For example, in a street scene, pixels may be labeled as road, car, pedestrian, building, etc. Some of the benchmark datasets used for these techniques are:

• Cityscapes 

• PASCAL VOC

• NYU Depth v2

• S3DIS

Network Architecture

Deep learning models for segmentation typically leverage a foundational convolutional neural network (CNN) network as their core architecture. It is the most optimized approach for semantic segmentation. The network architecture consists of an encoder-decoder structure allowing hierarchical feature extraction and upsamplingA process of increasing the spatial resolution of an image or feature map to generate dense pixel-wise predictions. Some of the CNN based models that are getting recognized are:

• ResNet 

• VGG

• LeNet-5

• GoogLeNet (Inception)

Training

The annotated dataset that we had in the first step is used to train the network. During training, the input images are fed into the network, and the network learns to predict the corresponding pixel-level labels. This results in the model being trained with respect to the ground truth annotations with techniques such as backpropagation and gradient descent. These techniques help minimize the difference between the actual and predicted data.

Inference 

Once the model is trained and ready, it can be used for semantic segmentation on any unseen images. During inference, an input image is fed into the trained network, and the network predicts the class label for each pixel in the image. This produces a dense output map where each pixel is classified with a specific class label.

Post-processing

The output prediction map may undergo post-processing steps to refine the segmentation results. Techniques such as smoothing, edge detection, and connected component analysis can improve segmentation accuracy and remove minor artifacts or noise.

Challenges and limitations

Even after the groundbreaking advancements of computer vision, we still see that there are some challenges that this technique faces. Overcoming these would enable more robust and accurate segmentation in various practical applications. We have summarized these limitations and stated them below:

• Pixel-level annotation: Semantic segmentation relies on obtaining accurate and extensive pixel-level annotations, which can be challenging and costly for large-scale datasets.

• Class imbalance: Some classes in datasets may be over-represented or under-represented, negatively impacting the model's performance, as it may struggle to learn the difference between rare or imbalanced classes effectively.

• Boundary ambiguity: Boundaries between different object classes can be ambiguous, making it difficult for the model to segment such regions accurately.

• Computational requirements: Semantic segmentation demands computationally intensive operations, leading to time-consuming and resource-intensive computations, constraining real-time performance in specific applications.

• Semantic confusion: Discriminating between visually similar classes can be difficult, leading to misclassifications or errors in segmentation.

• Efficient memory usage: Deep learning models for semantic segmentation often require a massive memory volume to store model parameters and intermediate feature maps.

• Real-time performance: Achieving real-time or near-real-time performance in semantic segmentation can be challenging, especially when processing high-resolution video streams or in applications with strict latency requirements.

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Conclusion

In conclusion, semantic segmentation is a powerful computer vision technique enabling us pixel-level understanding and classification of entities in any frame. Despite challenges like computational requirements and data annotation, semantic segmentation continues to advance and find applications in fields such as autonomous driving, medical imaging, and object recognition. And with the future highly dependent on computer vision, techniques such as semantic segmentation have to improve as well.