Real-Time Object Detection

Real-time object detection aims to find and classify objects in images or video with low enough latency for applications such as driving, robotics, and surveillance. Common approaches include one-stage YOLO-style detectors and real-time DETR transformers, made more efficient through compact backbones, multi-scale feature fusion, distillation, quantization, and hardware-aware deployment. Performance is often measured on benchmarks such as COCO, VisDrone, and KITTI, alongside throughput and latency on GPUs or edge devices. A central challenge is balancing accuracy, speed, and energy use while maintaining reliability under domain shifts, crowded scenes, and sustained real-world operation.