One Stage Detection

One-stage detection methods aim to directly predict object classes and locations in a single step, offering faster inference compared to two-stage approaches. Current research focuses on improving the accuracy and robustness of one-stage detectors, particularly addressing challenges like cross-domain adaptation (handling variations in image data) and open-world scenarios (detecting unseen objects). This involves exploring novel architectures, such as those incorporating background-focused alignment or network stability analysis, and adapting existing models like YOLO and FCOS for enhanced performance. The efficiency and potential for real-time applications make one-stage detection highly significant for various fields, including autonomous driving and video analysis.