[Deep Learning] Target detection, instance segmentation, semantic segmentation comparison one by one

Question: What is the difference between target detection, instance segmentation and semantic segmentation?

Object Detection, Instance Segmentation and Semantic Segmentation are three related problems in the field of computer vision but with different tasks and goals. Here are their brief differences:

1. Object Detection:

   • Task description: The task of object detection is to locate and identify multiple objects in images or videos and provide a bounding box for each object.
   • Output: The output is a set of bounding boxes, each of which identifies an object in the image, and usually includes the object's class label.

2. Instance Segmentation:

   • Task description: Instance segmentation requires not only locating and identifying objects, but also assigning a specific label to each pixel in each object to distinguish different instances.
   • Output: The output is a set of segmentation masks, where each mask corresponds to an instance of an object, identifying the pixel-level location of the object.

3. Semantic Segmentation:

   • Task description: The task of semantic segmentation is to divide the image into multiple semantic regions, each region has the same semantic label, that is, classify each pixel in the image as belonging to a specific category.
   • Output: The output is an image with each pixel assigned a semantic category label.

Summarize:

• Object Detection: focuses on detecting and localizing multiple objects in an image, and the output is a bounding box and class label.
• Instance segmentation: Not only focuses on the localization and recognition of objects, but also focuses on pixel-level segmentation of objects, and the output is a segmentation mask for each object.
• Semantic Segmentation: focuses on dividing the image into semantic regions, with each pixel assigned a semantic category label.

The difference between instance segmentation and semantic segmentation

Let us explain the difference between instance segmentation and semantic segmentation in more detail with a more concrete example:

1. Semantic Segmentation:

   • Task description: Classify each pixel in an image as belonging to a specific semantic category. Each pixel is labeled as an object or region in the image, but it is not distinguished whether these objects or regions belong to different instances.
   • Example: If there is a cat and a dog in the image, semantic segmentation will assign the label "cat" or "dog" to each pixel in the image, but not Distinguish that they belong to different independent instances.

2. Instance Segmentation:

   • Task description: In addition to semantic segmentation of each pixel in the image, instance segmentation also requires distinguishing different instances of different objects. This means assigning a unique identity to each independent object, even if they belong to the same semantic category.
   • Example: If there are two cats and a dog in the image, instance segmentation will assign a different identity to each cat and dog and provide a pixel level for each object segmentation.

Summarize:

• Semantic Segmentation: focuses on semantically classifying each pixel in an image, without distinguishing between different instances within the same semantic category.
• Instance Segmentation: In addition to semantically classifying each pixel in the image, it also distinguishes between different instances of different objects, providing a unique identity for each object.

What is the difference between object detection and instance segmentation?

Object Detection and Instance Segmentation are two related but different tasks in computer vision.

1. Target Detection:

   • Task description: The task of object detection is to detect and locate multiple objects in images or videos, and provide a bounding box (Bounding Box) for each object. Object detection does not distinguish between instances of different objects and only focuses on the presence and location of objects.
   • Output: The output is a set of bounding boxes, each representing an object in the image, and usually a class label for the object.

2. Instance splitting:

   • Task description: Instance segmentation requires not only detecting and localizing objects, but also assigning a unique label to each pixel of each object to distinguish different instances of different objects. Instance segmentation provides not only the location of objects but also a pixel-level segmentation of each object.
   • Output: The output is a set of segmentation masks, where each mask corresponds to an instance of an object, identifying the pixel-level location of the object.

Summarize:

• Object Detection: focuses on detecting and localizing multiple objects in an image, and the output is a bounding box and class label. Does not provide pixel-level segmentation of objects.
• Instance Segmentation: not only focuses on the positioning of objects, but also distinguishes different instances of different objects, providing pixel-level segmentation masks for each object.

The key to distinguishing the two is that object detection provides coarse location information (bounding boxes) of objects, while instance segmentation provides more detailed object segmentation, including pixel-level labels for each object.

Which one should be used for defect detection?

Flaw detection typically involves detecting anomalies or defects in images. Choosing to use object detection, instance segmentation, or semantic segmentation depends on your specific needs and the nature of your problem. Here are some suggestions:

1. Target Detection:

   • Use for: If you wish to detect multiple different types of defects in an image and provide a bounding box and class label for each defect.
   • Advantages: Object detection is suitable for scenes with multiple objects, each of which may have different categories. This makes it very effective for detecting many different types of flaws in images.

2. Instance splitting:

   • Used when: If you wish to detect and accurately segment every instance of a flaw in an image.
   • Advantages: Instance segmentation is suitable for scenarios where different instances need to be distinguished. If the same type of imperfections can appear in an image at the same time, and you need to analyze and process them independently, instance segmentation may be a better option.

3. Semantic segmentation:

   • Used when: If you are only interested in the overall location of imperfections present in the image and do not need to differentiate between different instances of them.
   • Advantages: Semantic segmentation is useful for a holistic understanding of the distribution of defects in an image. If your task is to detect abnormal regions in the overall image without requiring detailed knowledge of the independence of each anomaly, then semantic segmentation may be sufficient.

I currently feel that instance segmentation will be more suitable for defect detection!