首个面向汽车多任务异常检测的综合数据集，助力制造质量评估。
CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection
- 构建跨7个车域、3类任务的多任务异常检测数据集
- 100+图像支持少样本异常学习，验证多任务协同效果
- 适合工业质检与多任务学习研究者使用
汽车制造质量评估中的多任务视觉异常检测至关重要。然而现有方法仍局限于单一任务，缺乏统一的多任务评估基准。为此，我们提出CAD数据集，一个大规模、全面的汽车相关多任务视觉异常检测基准。该数据集包含超过100张图像，覆盖7个车辆领域和3项任务，为模型提供全面的异常检测视角。它是首个专为多任务学习（MTL）设计的汽车异常检测数据集，同时结合合成数据增强以支持少样本异常图像学习。我们建立了多任务基线并进行了广泛的实证研究。结果表明，多任务学习促进任务间交互与知识迁移，但也暴露了任务间的挑战性冲突。CAD数据集可作为标准化平台，推动汽车相关多任务视觉异常检测的未来发展。
原文摘要 · Abstract (English)
Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hindered by the absence of a unified benchmark for multi-task evaluation. To fill in this gap, We present the CAD Dataset, a large-scale and comprehensive benchmark designed for car-related multi-task visual anomaly detection. The dataset contains over 100 images crossing 7 vehicle domains and 3 tasks, providing models a comprehensive view for car-related anomaly detection. It is the first car-related anomaly dataset specialized for multi-task learning(MTL), while combining synthesis data augmentation for few-shot anomaly images. We implement a multi-task baseline and conduct extensive empirical studies. Results show MTL promotes task interaction and knowledge transfer, while also exposing challenging conflicts between tasks. The CAD dataset serves as a standardized platform to drive future advances in car-related multi-task visual anomaly detection.
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