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Semantic Segmentation And Instance Segmentation Annotation

ByteBridge Semantic Segmentation

Segmentation is commonly referred to semantic segmentation. In short, it is the classification of all pixel points on an image, i.e., each pixel in an image is classified into a corresponding category to achieve pixel-level classification.

The high-level semantic labels refer to various object categories (e.g., people, roads, cars, etc.) and background categories (e.g., sky) in the image. High-accuracy classifying and positioning are required in the project. By accurately drawing the object's outline and accurately classifying the region inside, the specific object can be well separated from the background.

Instance segmentation is not only about pixel-level classification, but also about distinguishing different objects in the same categories.

ByteBridge Instance Segmentation

For example, if the image has multiple boxes, A, B, and C. The outputs of semantic segmentation are boxes, while the outputs of instance segmentation are different objects.

  • Autonomous Driving

For autonomous driving, a good instance segmentation system is very important for its core algorithm technology. The entire operation process is that when the vehicle cameras or LiDAR capture images and input them into the neural network model, the computer backend will automatically segment the images into classes for vehicle systems recognition so as to avoid obstacles. These classes could be pedestrians, vehicles, buildings, sky, vegetation, etc. In one word, instance segmentation can help self-driving vehicles to identify drivable areas in an image.

  • GIS

For GIS, by training neural networks model, instance segmentation allows machines to input satellite remote sensing images, to automatically identify roads, rivers, crops, buildings, etc., and to label each pixel in the image. For example, ecosystem changes on a map can be detected. Instance segmentation also plays a big role in satellite map navigation.

  • Medical Care

In the field of intelligent medical care such as medical image analysis, instance segmentation is mainly applicated in tumor image segmentation, dental caries diagnosis, etc. Applying new technologies to such fields can help doctors to work better.

  • Industrial Robotics

In industrial robotics, especially in automated assembly work, instance segmentation can detect and identify defects in different scenarios, which can improve the efficiency of inspection, sorting, and storage in manufacturing work and reduce labor costs.

Labeling type: instance segmentation

Labeling target: the black box on the shelf and the objects in the box

ByteBridge Instance Segmentation


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