Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review

• URL: http://arxiv.org/abs/2501.11310v1
• Date: Mon, 20 Jan 2025 07:24:39 GMT
• Title: Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review
• Authors: Abdelrahman Alzarooni, Ehtesham Iqbal, Samee Ullah Khan, Sajid Javed, Brain Moyo, Yusra Abdulrahman,
• Abstract summary: Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Traditional anomaly detection workflow is based on a manual inspection by human operators. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision.
• Score: 4.139740414165092
• License:
• Abstract: Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.

Related papers

• Generative AI in Industrial Machine Vision -- A Review [0.0]
  generative AI demonstrates promising potential by improving pattern recognition capabilities. The application of generative AI in machine vision is still in its early stages due to challenges in data diversity, computational requirements, and the necessity for robust validation methods. A literature review based on the PRISMA guidelines was conducted, analyzing over 1,200 papers on generative AI in industrial machine vision.
  arXiv  Detail & Related papers  (2024-08-20T12:14:18Z)
• IPAD: Industrial Process Anomaly Detection Dataset [71.39058003212614]
  Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames. We propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage.
  arXiv  Detail & Related papers  (2024-04-23T13:38:01Z)
• A Systematic Review of Available Datasets in Additive Manufacturing [56.684125592242445]
  In-situ monitoring incorporating visual and other sensor technologies allows the collection of extensive datasets during the Additive Manufacturing process. These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning. This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria.
  arXiv  Detail & Related papers  (2024-01-27T16:13:32Z)
• Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping [0.0]
  Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data.
  arXiv  Detail & Related papers  (2023-07-28T20:58:00Z)
• Deep Industrial Image Anomaly Detection: A Survey [85.44223757234671]
  Recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD) In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques. We highlight several opening challenges for image anomaly detection.
  arXiv  Detail & Related papers  (2023-01-27T03:18:09Z)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images [2.4976719861186845]
  In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection.
  arXiv  Detail & Related papers  (2022-04-24T01:38:18Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• Cognitive Visual Inspection Service for LCD Manufacturing Industry [80.63336968475889]
  This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. System is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture.
  arXiv  Detail & Related papers  (2021-01-11T08:14:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.