In this work, we introduce a novel signal-versus-noise identification tool based on the YOLO (You Only Look Once) object detection framework. This tool provides scene identification capabilities and essential information regarding whether an observed transient is noise or signal. Additionally, it supplies detailed time-frequency coordinates in the form of pixel masks. The simultaneous identification of noise and signal, combined with precise pixel-level localization, represents a significant advancement in gravitational-wave data analysis. By rigorously validating our approach on real LIGO detector data, we demonstrate the potential of this method to improve the accuracy and reliability of gravitational-wave detections, thereby contributing meaningfully to the broader objectives of gravitational-wave astronomy.