Object Detection
Object detection is a computer vision task that identifies and locates multiple objects within an image by predicting bounding boxes and class labels. YOLO, Faster R-CNN, and DETR are popular object detection models.
Understanding Object Detection
Object detection is a computer vision task that involves both identifying and localizing specific objects within images or video frames by drawing bounding boxes around them and assigning class labels. Unlike image classification, which assigns a single label to an entire image, object detection must handle multiple objects of varying sizes and positions simultaneously. Landmark architectures include YOLO (You Only Look Once), which processes images in real time, and Faster R-CNN, which uses region proposals for high accuracy. Object detection powers critical applications such as autonomous vehicle perception systems, security surveillance, retail inventory management, and medical imaging analysis. Modern approaches leverage deep convolutional neural networks with feature pyramid networks to detect objects at multiple scales. The field continues to advance with transformer-based detectors like DETR that eliminate hand-crafted components like anchor boxes.
Category
Computer Vision
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Bounding Box
A bounding box is a rectangular border drawn around an object in an image to indicate its location and extent. Bounding boxes are the primary output format for object detection models.
Computer Vision
Computer vision is a field of AI that enables machines to interpret and understand visual information from images and videos. Applications include facial recognition, autonomous driving, medical imaging, and augmented reality.
Face Recognition
Face recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features in images or video. It is used in security systems, phone unlocking, and photo organization.
Image Captioning
Image captioning is the AI task of generating natural language descriptions of images. It requires both visual understanding (computer vision) and text generation (NLP) capabilities.
Image Classification
Image classification is the computer vision task of assigning a label to an entire image based on its visual content. Deep learning models like ResNet and Vision Transformers achieve near-human accuracy on this task.
Image Segmentation
Image segmentation is the process of partitioning an image into meaningful regions or classifying each pixel into a category. It is used in medical imaging, autonomous driving, and satellite analysis.
Instance Segmentation
Instance segmentation is a computer vision task that identifies each object in an image and delineates its exact pixel boundary. Unlike semantic segmentation, it distinguishes between individual instances of the same class.
Masked Autoencoder
A masked autoencoder is a self-supervised learning method that masks random patches of an image and trains the model to reconstruct them. It has proven highly effective for pre-training vision models.