Year: 2025
Type: conference-paper
Abstract: The confluence of computer vision and natural language processing has yielded powerful vision language models (VLMs) capable of multimodal understanding. We applied state-of-the-art VLMs for quality monitoring of the shoe assembly industry. By leveraging the ability of VLMs to jointly process visual and textual data, we developed a system for automated defect detection and contextualized feedback generation to enhance the efficiency and consistency of quality assurance processes. We conducted an empirical evaluation by evaluating the effectiveness of the developed VLM system in identifying standard procedures for assembly, using the video data from a shoe assembly line. The experimental results validated the potential of the VLM system in detecting the quality of footwear assembly, highlighting the feasibility of future practical deployment in industrial quality control scenarios.
Authors: Hsin-Ming Tseng, Hsueh-Ting Chu
Institution: Asia University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414523592