Despite their great accuracy, neural networks are not very popular in fields like medical, finance, education, and others where predictive explainability are essential. The objective of this work is to create and train a model using PyTorch Pipeline that divides photos into “Good” and “Anomaly” classes and, if the image is categorized as an “Anomaly,” a bounding box is returned for the fault. While this work appears straightforward and similar to other item detection tasks, there is a problem that it lacks bounding box labels. Fortunately, this problem can be solved by the model in the inference mode, trained without labels for defective regions, and is able to forecast a bounding box for a defective region in the picture, by processing feature maps from the deep convolutional layers. This work discusses the strategy and talks about how to use it for the purpose of defect detection in the real world. A 400-image dataset that includes pictures of both perfect objects (classed as “good”) and imperfect objects (classed as “anomalies”) has been used. The dataset is unbalanced; there are more examples of good than bad photographs. Any form of object, such as a bottle, cable, pill, tile, piece of leather, a zipper, etc., may be seen in the images.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Automatic Visual Inspection - Defects Detection using CNN


    Beteiligte:


    Erscheinungsdatum :

    01.12.2022


    Format / Umfang :

    679872 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AUTOMATIC SYSTEM OF VISUAL INSPECTION

    DZHUNG SUN DZH | Europäisches Patentamt | 2016

    Freier Zugriff

    Fuel Pellets Automatic Visual Inspection

    Aguirre, F. / Domingo, A. / Italian Society for Non-destructive Testing Monitoring Diagnostics | British Library Conference Proceedings | 2000


    Automatic visual inspection and defect detection on variable data prints

    Vans, M. / Schein, S. / Staelin, C. et al. | British Library Online Contents | 2011


    UAS-based autonomous visual inspection of airplane surface defects

    Tappe, Mark / Dose, Daniel / Oelsch, Martin et al. | SPIE | 2022