用合成数据+视觉模型实现汽车质检，省去大量真实标注
Synthetic Similarity Search in Automotive Production
- 用DINOv2提取图像特征，通过余弦距离比对参考图
- 在8个真实场景中达到生产级准确率，无需真实参考数据
- 适合缺乏标注数据的工业质检场景
汽车生产中的视觉质量检测对车辆安全至关重要。计算机视觉因其成本低、可靠性高而被广泛应用，但其模型需大量标注数据，收集过程耗时且昂贵。为减少对真实训练数据的依赖，我们提出一种结合基于视觉基础模型的相似性搜索与合成数据的图像分类新流程。该方法利用DINOv2将输入图像转换为特征向量，并通过余弦距离与预分类的参考图像进行比较。通过使用合成数据而非真实图像作为参考，该流程在不依赖真实数据的情况下实现了高分类准确率。我们在八个实际检测场景中评估了该方法，结果表明其满足生产环境的高性能要求。
原文摘要 · Abstract (English)
Visual quality inspection in automotive production is essential for ensuring the safety and reliability of vehicles. Computer vision (CV) has become a popular solution for these inspections due to its cost-effectiveness and reliability. However, CV models require large, annotated datasets, which are costly and time-consuming to collect. To reduce the need for extensive training data, we propose a novel image classification pipeline that combines similarity search using a vision-based foundation model with synthetic data. Our approach leverages a DINOv2 model to transform input images into feature vectors, which are then compared to pre-classified reference images using cosine distance measurements. By utilizing synthetic data instead of real images as references, our pipeline achieves high classification accuracy without relying on real data. We evaluate this approach in eight real-world inspection scenarios and demonstrate that it meets the high performance requirements of production environments.
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