Purpose

This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition.

Design/methodology/approach

The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders.

Findings

The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models.

Originality/value

A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.

中文翻译：

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一种基于双编码器-解码器生成对抗网络的异常检测方法

目的

本研究旨在解决为机器人训练检测模型以检测工业环境中异常样本的挑战，而在这种情况下异常模式非常罕见。

设计/方法/方法

作者提出了一种新模型，该模型具有双编码器 - 解码器 (DED) 生成对抗网络，可在模型在没有任何异常模式的情况下进行训练时检测异常情况。DED 方法用于将高维输入图像映射到低维空间，通过该空间获得潜在变量。在训练过程中最小化潜在变量的变化有助于模型学习数据分布。异常检测是通过计算从两个编码器获得的两个低维向量之间的距离来实现的。

发现

与传统的异常检测模型相比，所提出的方法具有更好的准确性和 F1 分数。

原创性/价值

具有 DED 管道的新架构旨在捕获训练过程中的图像分布，以便准确识别异常样本。引入了一个新的权重函数来控制编码重建和对抗阶段的损失比例，以获得更好的结果。提出了一种异常检测模型，以针对现有的最先进方法实现卓越的性能。