Object Detection

Object detection asks models to identify what objects are present and where they appear, usually by predicting labeled bounding boxes in images or video. Common approaches include fast one-stage CNN detectors such as YOLO, proposal-based Faster R-CNN, and end-to-end transformer detectors such as DETR; feature pyramids and attention help handle objects at different scales. Work also explores oriented and 3D detection, sensor fusion, open-vocabulary recognition, and learning with limited or changing labels, with COCO, VisDrone, DOTA, KITTI, and nuScenes among the benchmarks used. A central challenge is transferring strong benchmark results to small, occluded, or unfamiliar objects and real-world conditions while keeping models efficient and evaluations reliable despite incomplete annotations.