Large unlabeled data and difficult-to-identify anomalies are the urgent
issues need to overcome in most industrial scene. In order to address this
issue, a new meth-odology for detecting surface defects in in-dustrial settings
is introduced, referred to as Memory Augmentation and Pseudo-Labeling(MAPL).
The methodology first in-troduces an anomaly simulation strategy, which
significantly improves the model's ability to recognize rare or unknown
anom-aly types by generating simulated anomaly samples. To cope with the
problem of the lack of labeling of anomalous simulated samples, a
pseudo-labeler method based on a one-classifier ensemble was employed in this
study, which enhances the robustness of the model in the case of limited
labeling data by automatically selecting key pseudo-labeling hyperparameters.
Meanwhile, a memory-enhanced learning mechanism is introduced to effectively
predict abnormal regions by analyzing the difference be-tween the input samples
and the normal samples in the memory pool. An end-to-end learning framework is
employed by MAPL to identify the abnormal regions directly from the input data,
which optimizes the ef-ficiency and real-time performance of de-tection. By
conducting extensive trials on the recently developed BHAD dataset (in-cluding
MVTec AD [1], Visa [2], and MDPP [3]), MAPL achieves an average im-age-level
AUROC score of 86.2%, demon-strating a 5.1% enhancement compared to the
original MemSeg [4] model. The source code is available at
this https URL

在工业场景中，针对大量未标记数据和难以识别的异常问题，本研究引入了一种名为记忆增强与伪标记的新方法（MAPL），通过模拟异常样本、使用基于单分类器组合的伪标记方法以及引入记忆增强学习机制，实现了直接从输入数据识别异常区域，优化了检测的效率和实时性，在最新的 BHAD 数据集上取得了 86.2％的平均图像级 AUROC 分数，相比原始 MemSeg 模型提高了 5.1％。

半监督异常检测的记忆增强和伪标记

MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised  Anomaly Detection

One of the most pressing challenges prevalent in the steel manufacturing
industry is the identification of surface defects. Early identification of
casting defects can help boost performance, including streamlining production
processes. Though, deep learning models have helped bridge this gap and
automate most of these processes, there is a dire need to come up with
lightweight models that can be deployed easily with faster inference times.
This research proposes a lightweight architecture that is efficient in terms of
accuracy and inference time compared with sophisticated pre-trained CNN
architectures like MobileNet, Inception, and ResNet, including vision
transformers. Methodologies to minimize computational requirements such as
depth-wise separable convolution and global average pooling (GAP) layer,
including techniques that improve architectural efficiencies and augmentations,
have been experimented. Our results indicate that a custom model of 590K
parameters with depth-wise separable convolutions outperformed pretrained
architectures such as Resnet and Vision transformers in terms of accuracy
(81.87%) and comfortably outdid architectures such as Resnet, Inception, and
Vision transformers in terms of faster inference times (12 ms). Blurpool fared
outperformed other techniques, with an accuracy of 83.98%. Augmentations had a
paradoxical effect on the model performance. No direct correlation between
depth-wise and 3x3 convolutions on inference time, they, however, they played a
direct role in improving model efficiency by enabling the networks to go deeper
and by decreasing the number of trainable parameters. Our work sheds light on
the fact that custom networks with efficient architectures and faster inference
times can be built without the need of relying on pre-trained architectures.

提出了一种轻量级的深度学习模型用于在钢铁制造行业中识别表面缺陷，与 MobileNet、Inception、ResNet 和 Vision transformers 等预训练卷积神经网络相比，该新模型具有更高的准确性和更快的推理速度，并采用了深可分离卷积，全局平均池化层和改进的架构高效性增强技术。

金属铸造缺陷检测的高效神经网络方法

Efficient Neural Net Approaches in Metal Casting Defect Detection

Under the semi-supervised framework, we propose an end-to-end memory-based
segmentation network (MemSeg) to detect surface defects on industrial products.
Considering the small intra-class variance of products in the same production
line, from the perspective of differences and commonalities, MemSeg introduces
artificially simulated abnormal samples and memory samples to assist the
learning of the network. In the training phase, MemSeg explicitly learns the
potential differences between normal and simulated abnormal images to obtain a
robust classification hyperplane. At the same time, inspired by the mechanism
of human memory, MemSeg uses a memory pool to store the general patterns of
normal samples. By comparing the similarities and differences between input
samples and memory samples in the memory pool to give effective guesses about
abnormal regions; In the inference phase, MemSeg directly determines the
abnormal regions of the input image in an end-to-end manner. Through
experimental validation, MemSeg achieves the state-of-the-art (SOTA)
performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the
image-level and pixel-level, respectively. In addition, MemSeg also has a
significant advantage in inference speed benefiting from the end-to-end and
straightforward network structure, which better meets the real-time requirement
in industrial scenarios.

本研究提出了一种基于内存的端到端分割网络（MemSeg），该网络引入了人工模拟的异常样本和记忆样本以辅助网络的学习。经过实验验证，该方法在检测工业产品表面缺陷方面取得了最先进的性能。