Visual Language Model as a Judge for Object Detection in Industrial Diagrams
This addresses the need for automated quality assessment in digitalizing industrial diagrams for applications like digital twins, though it is incremental as it builds on existing VLM capabilities.
The paper tackled the problem of evaluating object detection quality in industrial diagrams by introducing a framework that uses Visual Language Models to assess and refine detections, improving performance on complex diagrams.
Industrial diagrams such as piping and instrumentation diagrams (P&IDs) are essential for the design, operation, and maintenance of industrial plants. Converting these diagrams into digital form is an important step toward building digital twins and enabling intelligent industrial automation. A central challenge in this digitalization process is accurate object detection. Although recent advances have significantly improved object detection algorithms, there remains a lack of methods to automatically evaluate the quality of their outputs. This paper addresses this gap by introducing a framework that employs Visual Language Models (VLMs) to assess object detection results and guide their refinement. The approach exploits the multimodal capabilities of VLMs to identify missing or inconsistent detections, thereby enabling automated quality assessment and improving overall detection performance on complex industrial diagrams.