Image segmentation and object detection are fundamental tasks in computer vision with distinct goals. Object detection identifies and localizes objects within an image by drawing bounding boxes around them, classifying each object. This is widely used in applications like autonomous driving and surveillance. In contrast, image segmentation involves partitioning an image into multiple segments or regions, where each pixel is assigned a class label. Semantic segmentation classifies pixels into categories, while instance segmentation distinguishes different objects of the same class. Together, these tasks enable machines to understand visual data with precision, supporting applications from medical imaging to robotics.
Semantic segmentation is a computer vision task that involves classifying each pixel in an image into predefined categories, helping machines understand the detailed structure of a scene. Popular architectures for semantic segmentation include Fully Convolutional Networks (FCNs), U-Net, and DeepLab (Fully Connected CRFs in particular), which utilize convolutional layers to capture spatial hierarchies in the data. U-Net, particularly, is known for its success in medical imaging due to its encoder-decoder structure. Common datasets used for training and benchmarking these models include Cityscapes for urban scene segmentation, PASCAL VOC, and COCO, which provide diverse, annotated images for fine-grained pixel-level analysis. Refer to the following resources for getting an insight into segmentation tasks:
Object detection is a key task in computer vision that involves identifying and localizing objects within an image by drawing bounding boxes and assigning class labels to them. Several architectures have been developed to efficiently perform object detection, with popular models including YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN. YOLO is known for its speed, processing images in real-time by predicting bounding boxes and class probabilities in a single pass. SSD balances accuracy and speed by using feature maps at different scales, while Faster R-CNN improves accuracy with a region proposal network. These models are widely used in applications like autonomous vehicles, surveillance, and smart devices.