In recent years, artificial intelligence has been applied in the industry to automate various vision-based applications, such as monitoring structural defects in manufactured products. For industrial inspections, the automatic detection and localization of defective parts from product images ensure quality while avoiding waste of labor and materials. To this end, this paper introduces a two-branch neural network architecture that comprises detector and localizer components, where the former identifies the presence of defects, while the latter defines the region of interest for each defective area detected in the product structure. In both cases, the underlying strategy lies in a semi-supervised setting observing only defect-free product images, enabling the learning of the correct product structure that can be used to identify every kind of defect independently from position, color, or shape. The effectiveness of the proposed method is evaluated on the MVTec-AD industrial benchmark comprising different object and texture categories, considering the common state-of-the-art AUROC and SSIM metrics for the evaluation of anomaly detection and localization, respectively. Ablation studies varying the number of layers are performed on all the architecture components, founding that the presented two-branch network is consistently robust among all classes achieving remarkable results, i.e., 98% for AUROC and 94% for SSIM. What is more, measuring the time required to detect and localize the defects, the trained network is run on the RPi4B as an embedded system to simulate a practical industrial setting with limited computational resources, demonstrating the applicability of the presented method in real scenarios.

近年来，人工智能已在行业中应用，以自动化各种基于视觉的应用，例如监控制造产品中的结构缺陷。对于工业检测，从产品图像中自动检测和定位缺陷部件可确保质量，同时避免劳动力和材料的浪费。为此，本文介绍了一种包含检测器和定位器组件的双分支神经网络架构，其中前者识别缺陷的存在，而后者定义产品结构中检测到的每个缺陷区域的感兴趣区域。在这两种情况下，基本策略都在于仅观察无缺陷产品图像的半监督设置，能够学习正确的产品结构，该结构可用于独立于位置、颜色或形状来识别各种缺陷。所提出方法的有效性在包含不同对象和纹理类别的 MVTec-AD 工业基准上进行了评估，分别考虑了用于评估异常检测和定位的常见最新 AUROC 和 SSIM 指标。对所有架构组件进行了不同层数的消融研究，发现所提出的双分支网络在所有类别中始终保持稳健，取得了显着的结果，即 AUROC 为 98%，SSIM 为 94%。更重要的是，测量检测和定位缺陷所需的时间，