How YOLOv8 is used to obtain the contours/object and calculate individual area
Just googling your exact question gives multiple very good answers, you should try it. If that's not detailed enough for you, I guess you should post a more precise question.
Dear Debmalya Ray,
Segmentation provides fine-grained information about object boundaries and regions, while detection focuses on identifying specific objects and their locations.
https://keymakr.com/blog/semantic-segmentation-vs-object-detection-understanding-the-differences/#:~:text=Segmentation%20provides%20fine%2Dgrained%20information,specific%20objects%20and%20their%20locations.
Regards,
Shafagat
Object Detection
Object detection is the process of identifying and locating objects within an image or video. The primary goal here is to determine what objects are present in the image and where they are located. Here are some key points about object detection:
- Bounding Boxes: Object detection typically involves drawing bounding boxes around objects. Each box is usually accompanied by a label (to identify the class of the object) and sometimes a confidence score (indicating the model's certainty about the detection).
- Multiple Objects and Classes: Detection models are generally trained to identify multiple classes of objects and can detect multiple objects in a single frame.
- Metrics: Performance is often measured in terms of precision and recall across different object classes and how well the bounding boxes match the ground truth data.
- Popular Models: Some well-known object detection models include YOLO (You Only Look Once), SSD (Single Shot MultiDetector), and Faster R-CNN.
Object Segmentation
Object segmentation goes a step further by not only detecting objects but also delineating the precise boundaries of each object. It can be divided into two main types:
- Pixel-level Accuracy: Segmentation requires models to predict the class of each pixel, thus providing a pixel-wise mask for each object.
- More Granular: This task is more granular than object detection and is useful in scenarios where precise spatial understanding and context are required.
- Popular Models: Common models for object segmentation include Mask R-CNN (for instance segmentation) and U-Net (for semantic segmentation), among others.
Use Cases Differences
- Object Detection is typically used in applications where knowing the location of objects within the larger context of the scene is sufficient, such as in surveillance systems, self-driving car systems, and simple categorization tasks.
- Object Segmentation is used in applications requiring a detailed understanding of the scene at the pixel level, such as in medical imaging, where precise boundaries around pathologies are needed, or in augmented reality, where interactions between virtual and real objects depend on precise object contours.
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