Object detection in technical drawings for data-driven design: the case of patents

ABSTRACT:

Data-Driven Design (DDD) is emerging as a transformative approach in engineering design, leveraging AI tools to extract knowledge from design data that drive product development and innovation. While large language models have advanced DDD through the analysis of textual data, technical drawings remain largely unexplored. To address the limitations of current vision-language models, this study presents a novel object detection pipeline that automatically identifies components in patent images, enabling data-driven analysis of component geometries, interfaces, and spatial configurations.