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Object detection simultaneously classifies object instances and localizes them within an image or video, typically using bounding boxes.
Object detection is a computer vision task that identifies individual instances of objects belonging to specified semantic classes in images or videos. It determines both what an object is, such as a person, car, face, or building, and where it is located, usually by drawing a bounding box around it. Detection relies on visual features such as shape, texture, color, component relationships, and spatial measurements; for example, facial detection may use the arrangement of the eyes, nose, and mouth. Object detection is used in image annotation, vehicle counting, activity recognition, surveillance, object tracking, autonomous driving, and image retrieval. Performance is commonly evaluated using intersection over union (IoU), precision, recall, average precision (AP), and mean average precision (mAP). Methods include traditional feature-based systems, such as Haar features, SIFT, and HOG combined with classifiers, as well as end-to-end neural approaches based on convolutional neural networks and vision transformers. Representative models include R-CNN and its variants, YOLO, SSD, RetinaNet, and DETR.
Object detection simultaneously classifies object instances and localizes them within an image or video, typically using bounding boxes.
Detection systems use visual characteristics such as shape, texture, color, and the spatial relationships between object parts.
Common applications include surveillance, tracking, autonomous driving, image annotation, vehicle counting, facial detection, and activity recognition.
Intersection over union measures the overlap between a predicted bounding box and the ground-truth box; correct class labels and sufficient IoU are required for true positives.
Detection performance is often summarized with average precision for each class and mean average precision across all classes.
Approaches range from hand-engineered features with classifiers to end-to-end neural networks, including CNN-based detectors and transformer-based models.
A category of semantically similar objects, such as cars, people, faces, or traffic signs.
A particular occurrence of an object class within an image or video.
The process of identifying object instances, assigning them class labels, and locating them in visual data.
A rectangular region used to indicate the position and extent of a detected object.
A measure of overlap between a predicted bounding box and a ground-truth box, calculated as their intersection area divided by their union area.
The mean of the average precision scores calculated across all object classes.
A predicted object detection that does not sufficiently match a ground-truth object or has an incorrect class label.
A ground-truth object that the detection system fails to identify correctly.
A neural network architecture that learns visual features directly from images and is widely used for object detection.
Techniques used to reduce performance loss when training and testing images come from different data distributions.
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