Object detection is a core task in computer vision that identifies and localizes objects within images or video by predicting both class labels and bounding boxes. Modern approaches are dominated by deep learning, particularly convolutional neural networks and Transformers, which have replaced earlier hand crafted feature techniques.

Two main paradigms exist: two stage and one stage detectors. Two stage methods, such as those based on Region based CNNs, first generate region proposals that might contain objects and then classify and refine them. These methods typically achieve high accuracy but are computationally heavier. One stage detectors, such as the YOLO and SSD families, directly predict classes and bounding boxes on a dense grid over the image. They are faster and well suited for real time applications but historically had slightly lower accuracy, a gap that has narrowed with newer architectures.

Architectural advances include feature pyramids to handle objects at multiple scales, anchor based and anchor free designs for bounding box prediction, and attention mechanisms for better global context modeling. Loss functions have evolved to improve bounding box regression quality and handle class imbalance. Training typically relies on large labeled datasets such as COCO or Pascal VOC, with techniques like data augmentation, transfer learning, and fine tuning to adapt models to specific domains.

Current research addresses small object detection, robustness to occlusion, domain shift and adverse conditions, as well as efficiency for deployment on edge devices. Applications range from autonomous driving and robotics to medical imaging and industrial inspection. Trends point toward unified architectures that integrate detection with related tasks such as segmentation and tracking, and toward more efficient, data centric training pipelines.