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A Deep Dive into Using CNNs for Spotting Anomalies in Industrial Visual Checks: Methods and Real-World Applications Explored
Subject area: Science,Engineering and Technology · Area of research: Spotting Anomalies
Abstract
Recent advances in deep learning have made it possible for production lines to adopt automated and accurate anomaly detection in industrial visual inspections. Convolutional Neural Networks (CNNs), in particular, have shown superior performance over other models due to their ability to capture structured patterns in visual data. This paper presents a detailed survey of CNN-based approaches for identifying anomalies in industrial settings. Techniques are grouped into supervised, unsupervised, and self-supervised categories, with a focus on their strengths, limitations, and common use cases. The review also covers hybrid approaches that combine CNNs with generative models such as autoencoders and GANs to improve performance in data-scarce environments. A thorough catalog of available datasets is included, along with evaluation methods and comparative results across different CNN models in real-world industrial scenarios. Key deployment challenges are discussed, including limited data availability, domain shifts, model interpretability, and the need for real-time processing. Additionally, the paper highlights emerging trends and recommends future directions such as integrating Vision Transformers, leveraging contrastive learning, and prioritizing edge deployment. This survey aims to support professionals involved in building, implementing, or refining CNN-based anomaly detection systems in modern industrial operations.
Keywords
CNN, anomaly detection, industrial visual inspection, deep learning, autoencoder, GAN, real-time inspection, defect detection.
How to cite this paper
@article{1708999,
author = {Aditya Kinnori, Aniket Tripathi},
title = {A Deep Dive into Using CNNs for Spotting Anomalies in Industrial Visual Checks: Methods and Real-World Applications Explored},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {6},
pages = {150-163},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708999.pdf},
abstract = {Recent advances in deep learning have made it possible for production lines to adopt automated and accurate anomaly detection in industrial visual inspections. Convolutional Neural Networks (CNNs), in particular, have shown superior performance over other models due to their ability to capture structured patterns in visual data. This paper presents a detailed survey of CNN-based approaches for identifying anomalies in industrial settings. Techniques are grouped into supervised, unsupervised, and self-supervised categories, with a focus on their strengths, limitations, and common use cases. The review also covers hybrid approaches that combine CNNs with generative models such as autoencoders and GANs to improve performance in data-scarce environments. A thorough catalog of available datasets is included, along with evaluation methods and comparative results across different CNN models in real-world industrial scenarios. Key deployment challenges are discussed, including limited data availability, domain shifts, model interpretability, and the need for real-time processing. Additionally, the paper highlights emerging trends and recommends future directions such as integrating Vision Transformers, leveraging contrastive learning, and prioritizing edge deployment. This survey aims to support professionals involved in building, implementing, or refining CNN-based anomaly detection systems in modern industrial operations.},
keywords = {CNN, anomaly detection, industrial visual inspection, deep learning, autoencoder, GAN, real-time inspection, defect detection.},
month = {December},
}