Weak defect and flatness discrepancy fused identification for robotic measuring of putty repair area

In this study, we introduce a sophisticated automatic identification technique tailored for repair area detection on highspeed train body, with an emphasis on weak defects and flatness discrepancies within putty coatings. The methodology commences with developing a coating weak defect identification strategy, leveraging the Xception network and ASPP module for enhanced accuracy. This is followed by the integration of weak defect identification outcomes into the flatness geometric difference identification framework, effectively reducing aggregated point clouds’ interference on flatness assessments. The process culminates in the coating repair areas identification approach, designed to detect defects and necessary machining removals on the coating surface. Our results indicate that by incorporating the effects of defects on flatness into the analysis, the precision of flatness discrepancy detection is significantly improved. Moreover, the amalgamation of defect identification with the evaluation of flatness variations enables more precise mapping of repair zones, representing notable progress in the field of robotic repair technologies.