Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
<|reference_start|>Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization: In the realm of practical Anomaly Detection (AD) tasks, manual labeling of anomalous pixels proves to be a costly endeavor. Consequently, many AD methods are crafted as one-class classifiers, tailored for training sets completely devoid of anomalies, ensuring a more cost-effective approach. While some pioneering work has demonstrated heightened AD accuracy by incorporating real anomaly samples in training, this enhancement comes at the price of labor-intensive labeling processes. This paper strikes the balance between AD accuracy and labeling expenses by introducing ADClick, a novel Interactive Image Segmentation (IIS) algorithm. ADClick efficiently generates "ground-truth" anomaly masks for real defective images, leveraging innovative residual features and meticulously crafted language prompts. Notably, ADClick showcases a significantly elevated generalization capacity compared to existing state-of-the-art IIS approaches. Functioning as an anomaly labeling tool, ADClick generates high-quality anomaly labels (AP $= 94.1\%$ on MVTec AD) based on only $3$ to $5$ manual click annotations per training image. Furthermore, we extend the capabilities of ADClick into ADClick-Seg, an enhanced model designed for anomaly detection and localization. By fine-tuning the ADClick-Seg model using the weak labels inferred by ADClick, we establish the state-of-the-art performances in supervised AD tasks (AP $= 86.4\%$ on MVTec AD and AP $= 78.4\%$, PRO $= 98.6\%$ on KSDD2).<|reference_end|>
@article{li2024towards,
    title={Towards Efficient Pixel Labeling for Industrial Anomaly Detection and
  Localization},
    author={Hanxi Li, Jingqi Wu, Lin Yuanbo Wu, Hao Chen, Deyin Liu, Chunhua Shen},
    journal={arXiv preprint arXiv:2407.03130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03130},
    primaryClass={cs.CV}
}