Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

Filters

3
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Automated Optical Inspection Using Anomaly Detection and Unsupervised Defect Clustering
    ( 2020)
    Lehr, Jan
    ;
    Sargsyan, Arlik
    ;
    Pape, Martin
    ;
    Philipps, Jan
    ;
    Krüger, Jörg
    Neural networks have proven to be extraordinarily successful in many computer vision applications. But the approaches used to train neural networks require large datasets of annotated images, which requires a solid amount of human time to prepare those datasets. To facilitate the adoption of machine learning based technologies in industrial computer vision applications, this paper presents a two-step unsupervised learning approach for anomaly detection with further defect clusterization. In the first stage, the defects are not explicitly learned, but are interpreted as an anomaly or novelty based on the dataset of defect-free samples. In a second stage, the anomalies detected in the first stage are clustered in unsupervised manner and classified into meaningful categories by experts with process knowledge (e.g. critical or non-critical defect). This paper presents a first small dataset containing one industrial object with a complex shape. The object is made of aluminiu m and is shown both free of defects and defective. Based on this, recommendations are given for an acquisition setup for a large, extensive dataset. Most of the existing papers are studying the approaches for uniform surface (texture) inspection. The specifics of this research is to identify defects on rigid bodies, which exhibit highly non uniform texture in the image. State of the art methods were evaluated and improved to increase the classification accuracy. With a fine-tuned ResNet-18 it was possible to achieve 100% accuracy for defective and defect-free images.
  • Publication
    Development of a Fire Detection Based on the Analysis of Video Data by Means of Convolutional Neural Networks
    ( 2019)
    Lehr, Jan
    ;
    Gerson, Christian
    ;
    Ajami, Mohamad
    ;
    Krüger, Jörg
    Convolutional Neural Networks (CNNs) have proven their worth in the field of image-based object recognition and localization. In the context of this work, a fire detector based on CNNs has been developed that detects fire by analyzing video sequences. The major additions of this work will primarily be realized through the use of temporal information contained in the video sequences depicting fire. In contrast to state of the art fire detectors, a large image database with 160,000 images with an even distribution of positive and negative samples has been created. To be able to compare image-based and video-based approaches as objectively as possible, different image-based CNNs will be trained under the same conditions as the video-based networks within the scope of this work. It will be shown that video-based networks offer an advantage over conventional image-based networks and therefore benefit from the temporal information of fire. We have achieved a prediction accuracy of 96.82%.
  • Publication
    Classification of Similar Objects of Different Sizes Using a Reference Object by Means of Convolutional Neural Networks
    ( 2019)
    Lehr, Jan
    ;
    Schlüter, Marian
    ;
    Krüger, Jörg
    Part identification is relevant in many industrial applications, either for direct recognition of components or assemblies, either as a fully automated process or as an assistance system. Convolutional Neural Networks (CNNs) have proven their worth in image processing, especially in classification tasks. It therefore makes sense to use them for industrial applications. There are major problems with parts that look very similar and can only be identified by their size. In this paper we have considered a subset of screws that all conform to the same norm but are of different sizes. The implicit learning of the screw size is only possible if the images are taken in a fixed distance setup and larger screws are shown larger on the images. In this paper we show that CNNs are able to implicitly measure target objects with the help of reference objects and thus to integrate the object size into the learning process.