Case Study: SK Godelius achieves safer, more consistent mining conveyor inspections with Ultralytics YOLO

SK Godelius detects 3,400 conveyor defects with Ultralytics YOLO

SK Godelius, an engineering and robotics company, faced the challenge of performing hazardous and inconsistent manual inspections of mining conveyor belts. These inspections exposed workers to dangerous conditions and made it difficult to reliably track the thousands of components across kilometers of conveyors. To automate this process, they turned to Ultralytics and its YOLO models.

By implementing an autonomous robotic inspection system powered by Ultralytics YOLO, SK Godelius was able to automate defect and anomaly detection. The solution uses computer vision to identify failing components in real-time. This has resulted in the detection of over 3,400 anomalies, including 900 critical ones, while maintaining over 95% average weekly coverage. Ultralytics provided a reliable, data-driven process that improves safety, prevents costly downtime, and gives maintenance teams early warnings.

SK Godelius