Object Detection Benchmark

Object detection benchmarks evaluate the performance of algorithms that identify and locate objects within images or videos. Current research focuses on improving accuracy and generalization across diverse datasets and challenging scenarios, such as detecting small, occluded, or reflected objects, often employing transformer-based models and exploring techniques like multi-modal learning and self-supervised pre-training. These benchmarks are crucial for advancing object detection technology, impacting applications ranging from autonomous driving and robotics to medical image analysis and security systems. The development of more comprehensive and robust benchmarks, addressing issues like annotation quality and dataset bias, is a key area of ongoing work.