视觉语言模型作为工业图中目标检测的评判器
Visual Language Model as a Judge for Object Detection in Industrial Diagrams
AI总结：
针对工业图数字化中缺乏目标检测输出自动评估方法的问题，提出利用视觉语言模型（VLM）评估检测结果并指导优化的框架，可实现自动化质量评估并提升复杂工业图的检测性能。
摘要：
管道仪表图（P&ID）等工业图对工业工厂的设计、运行和维护至关重要。将这些图转化为数字形式是构建数字孪生、实现智能工业自动化的重要步骤。这一数字化过程中的核心挑战是精准的目标检测。尽管近期进展已显著提升了目标检测算法的性能，但目前仍缺乏自动评估其输出质量的方法。本文针对这一空白，提出了一个采用视觉语言模型（VLM）评估目标检测结果并指导其优化的框架。该方法利用VLM的多模态能力识别缺失或不一致的检测结果，从而实现自动化质量评估，并提升在复杂工业图上的整体检测性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
