Object detection and image segmentation are two related but distinct tasks in computer vision. Object detection is the task of identifying and localizing objects in an image or video. It involves predicting a bounding box around the objects of interest and classifying the objects within the bounding box. There are a variety of approaches to object detection, including traditional computer vision techniques, such as feature-based methods, and machine learning-based methods, such as deep learning. Image segmentation is the task of dividing an image into multiple segments or regions, each of which corresponds to a different object or background. Image segmentation is often used as a preprocessing step for other computer vision tasks, such as object detection or image recognition. There are a variety of approaches to image segmentation, including traditional computer vision techniques, such as edge detection and clustering, and machine learning-based methods, such as deep learning.
Both object detection and image segmentation are important tasks in computer vision and have a wide range of applications, including self-driving cars, robotics, and medical image analysis.
