Robust seam segmentation for robotic welding with transfer learning

In the field of industrial automation, robotic welding faces a recurring challenge: the correct identification of seams on highly reflective metal surfaces. Variable lighting conditions, specular flashes, and thin geometries make visual segmentation a complex problem for machine vision systems. Traditionally, deep learning models have attempted to solve this using increasingly heavy architectures, but this impacts inference speed and feasibility for real-time production environments. A promising alternative consists of optimizing lightweight networks such as BiSeNetV2 through transfer learning and hybrid loss functions that combine cross-entropy with the Lovász loss, achieving significant improvements in robustness against reflections without increasing computational resources. This approach, which recovers more than 96% of severe failure cases under reflective conditions, demonstrates that the key lies not in architectural complexity, but in an optimization strategy oriented toward learning stability. For companies seeking to implement similar solutions, the development of artificial intelligence for businesses becomes a differentiating factor. Companies like Q2BSTUDIO offer custom software services that integrate machine vision models adapted to hostile environments, complemented by custom applications that manage both data capture and decision-making. Furthermore, these systems are typically deployed on AWS and Azure cloud services, ensuring scalability and availability, while cybersecurity protects communications between robots and central platforms. In later stages, the generated data can be analyzed with business intelligence tools such as Power BI, allowing engineers to monitor weld quality in real time. It is also possible to incorporate AI agents that automate parameter adjustments based on segmentation performance, reducing human intervention. Thus, the combination of advanced machine learning techniques with a robust technological platform enables industries to achieve levels of precision that once seemed unattainable, without sacrificing speed or operational efficiency.