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The Argo Program is a global network of 4,000 autonomous drifting floats that provide essential, real-time data on the upper 2,000 meters of the ocean. By measuring temperature and salinity, Argo has become the primary source of information for monitoring ocean warming, sea-level rise, and climate variability. However, the massive volume of data generated—totaling millions of profiles—presents a significant challenge for Quality Control (QC).Traditionally, delayed-mode quality control has relied heavily on human expertise and the "trained eye" of scientists to identify instrumental drifts and sensor malfunctions. To address the cost and limitations of manual inspection, we introduce Argo-YOLO, an innovative approach that transposes computer vision techniques into the field of physical oceanography.By converting oceanographic profiles into graphical representations, our system utilizes the YOLO (You Only Look Once) deep learning architecture to "scan" the data, mimicking the visual diagnostic capabilities of expert oceanographers. This method enables high-speed, systematic detection of instrumental drifts, sensor malfunctions, and profile anomalies across the entire Argo dataset while maintaining the nuanced precision of human analysis.Initial results demonstrate that Argo-YOLO faithfully reproduces expert visual diagnostics with high performance: 97% accuracy in identifying valid profiles with only 3% false alarms, and 96% success in detecting anomalous profiles with 4% missed detections.These results confirm the viability of computer vision for operational oceanographic quality control.Argo-YOLO demonstrates how computer vision can be successfully adapted to oceanographic challenges, representing a major step toward automated, scalable quality control in global ocean observing systems and ensuring the integrity of long-term climate records in an era of "Big Data" oceanography.