As a key task in the field of computer vision, semantic segmentation is the only way to achieve complete scene understanding. In order for the machine to have vision, it has to go through the process of image classification, object detection and image segmentation. Among them, the technical difficulty of image segmentation is the highest. More and more applications benefit from image classification and segmentation technology, and full scene understanding is also crucial in the field of computer vision . Some of these applications include autonomous vehicles, human-computer interaction, AR-VR, and more. With the popularity of deep learning in recent years, many semantic segmentation problems are being solved using deep architectures, the most common of which is CNN (Convolutional Neural Network), which greatly exceeds other methods in terms of accuracy and efficiency.
What is Semantic Segmentation?
Semantic segmentation is a natural step from coarse-to-fine reasoning: the origin can be localized in classification, which involves making predictions over the entire input. The next step is localization/detection, which provides not only classes but also the space about those classes Additional information about the location. Finally, semantic segmentation enables fine-grained inference by densely predicting inferred labels for each pixel, so each pixel is labeled with its class that encloses the ore region of the object. More specifically, the goal of semantic image segmentation is to label each pixel in the image with the content represented by the corresponding class. Since we are making predictions for every pixel in the image, this task is often called dense prediction. The thing to note here is that semantic segmentation is the task of assigning a class to each pixel in a given image rather than assigning a single class to the entire image. Semantic segmentation is the task of assigning each pixel in an image to one of the kind.
Applications of Semantic Segmentation
Autonomous driving: It is necessary to equip the car with the necessary perception capabilities, so that the car can "observe" the road conditions and the surrounding environment, so that the self-driving car can safely drive on the road.
Medical Imaging Diagnostics: Machines can augment the analytical capabilities of radiologists, drastically reducing the time required for diagnostic testing.
Semantic Segmentation Task Construction Steps
- representative task
Takes an RGB color image (height × width × 3) or a grayscale image (height × width × 1), and outputs a segmentation map where each pixel contains a class label represented by an integer (height × width × 1).
- build a framework
A naive approach to building a neural network architecture for this task is to simply stack some convolutional layers (with the same padding to preserve dimensions), and then output a final segmentation map. This directly learns the mapping from the input image to its corresponding segmentation through continuous transformation of the feature map; however, it is quite computationally expensive to preserve the full resolution throughout the network.
- Upsampling method
Several different methods are used to upsample the resolution of the feature maps. The pooling operation downsamples the resolution by summarizing local regions with a single value (i.e., average or max pooling), while the "unpooling" operation works by assigning a single value to a higher , thus upsampling the resolution.
- fully convolutional network
At the end of 2014, Long et al. proposed a method of using a "full convolution" network for end-to-end, pixel-to-pixel training to complete image segmentation tasks.
- Add springboard connection
Solved by slowly upsampling the encoded representation (in stages), adding "skip connections" from earlier layers, and summing the two feature maps.
- Advanced U-Net variant
The standard U-Net model consists of a series of convolution operations for each "block" in the architecture.
- Define the loss function
Appen's Semantic Segmentation Tools and Platform
Appen has more than 25 years of experience in data labeling. Relying on the AI-assisted intelligent data labeling platform, we will provide customers with customized labeling teams and professional artificial intelligence experts for their projects, and use the "man-machine collaboration" method to provide deployment solutions for customers. High-quality training data required by AI models. Our data annotation platform provides a variety of annotation tools to cover different scenarios. Semantic segmentation tools can circle, paint and click, customize attribute sheets, solo mode and highly integrated quality inspection and delivery process. If you need smart annotation tools , please feel free to contact our experts.