Abstract
Industrial computer vision systems often struggle with noise, material variability, and uncontrolled imaging conditions, limiting the effectiveness of classical edge detectors and handcrafted pipelines. In this work, we present a language-guided generative vision system for remnant contour detection in manufacturing, designed to achieve CAD-level precision. The system is organized into three stages: data acquisition and preprocessing, contour generation using a conditional GAN, and multimodal contour refinement through vision-language modeling, where standardized prompts are crafted in a human-in-the-loop process and applied through image-text guided synthesis. On proprietary FabTrack datasets, the proposed system improved contour fidelity, enhancing edge continuity and geometric alignment while reducing manual tracing. For the refinement stage, we benchmarked several vision-language models, including Google's Gemini 2.0 Flash, OpenAI's GPT-image-1 integrated within a VLM-guided workflow, and open-source baselines. Under standardized conditions, GPT-image-1 consistently outperformed Gemini 2.0 Flash in both structural accuracy and perceptual quality. These findings demonstrate the promise of VLM-guided generative workflows for advancing industrial computer vision beyond the limitations of classical pipelines.
Abstract (translated)
工业计算机视觉系统经常面临噪声、材料变化和不受控制的成像条件等问题，这些问题限制了传统边缘检测器和手工制作流水线的有效性。在这项工作中，我们提出了一种语言引导的生成式视觉系统，用于制造过程中剩余轮廓的检测，旨在达到CAD级别的精度。该系统分为三个阶段：数据采集与预处理、使用条件GAN（生成对抗网络）进行轮廓生成以及通过视觉-语言建模实现多模式轮廓细化，在这一过程中，标准化提示在人机交互流程中设计并应用到图像文本引导合成中。 在专有的FabTrack数据集上，提出的系统提高了轮廓的保真度，增强了边缘连续性和几何对齐性，并减少了手动追踪的需求。对于细化阶段，我们比较了多种视觉-语言模型，包括Google的Gemini 2.0 Flash、OpenAI的GPT-image-1（在VLM引导的工作流程中整合）和开源基线模型。在标准化条件下，GPT-image-1在结构准确性和感知质量方面始终优于Gemini 2.0 Flash。 这些发现表明了视觉语言建模指导的生成工作流在工业计算机视觉领域超越传统管道限制方面的潜力。
URL
https://arxiv.org/abs/2509.00284
