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Anomaly detection and localization using deep learning(CAE)

version 1.0.1 (17.4 MB) by Takuji Fukumoto
You can learn how to detect and localize anomalies on image using Convolutional Auto Encoder.

609 Downloads

Updated 25 Dec 2020

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On shipping inspection for chemical materials, clothing, and food materials, etc, it is necessary to detect defects and impurities in normal products. However, it is difficult to collect enough abormal images to use for deep learning.
This demo shows how to detect and localize anomalies using CAE.
This method using only normal images for training may allow you to detect abnormalities that have never been seen before. By customizing SegNet model, you can easily get the network structure for this task.

[Japanese]
このデモでは正常な画像に紛れる異常をディープラーニングを用いて検出ならびに位置の特定を行えます。
正常な画像のみ使ってモデルを学習させるこの方法では,これまで見たことがない異常に対しても検出できる可能性があります。簡単にモデル構造を得るためにSegNetモデルをカスタムして利用しています。

[Keyward] 画像処理・画像分類・ディープラーニング・DeepLearning・IPCVデモ
・SegNet ・異常検出・外観検査・セマンティックセグメンテーション・オートエンコーダー・畳み込み

Cite As

Takuji Fukumoto (2021). Anomaly detection and localization using deep learning(CAE) (https://github.com/mathworks/Anomaly-detection-and-localization-using-CAE/releases/tag/1.0.1), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019a
Compatible with R2019a and later releases
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.