Change Detection: The Framework of Visual Inspection System for Railway Plug Defects

Du, Xinyu; Cheng, Yu; Gu, Zichen

doi:10.1109/access.2020.3017691

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…Four industrial cameras assume that the real-time image acquisition of the bearing on the mechanical transmission device is carried out on the bearing production line, and the collected image data is transmitted to the industrial computer software system [15].…”

Section: Image Acquisition Modulementioning

confidence: 99%

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Zhu

2022

Journal of Sensors

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In order to solve the problem that the traditional detection technology can not meet the requirements of online detection, a visual detection device for bearing inner ring defects based on image processing and pattern recognition is proposed in this paper. The device systematically designs an image acquisition device of bearing inner ring based on CCD. In the hardware scheme, the appropriate lens, camera, light source, and other related hardware are selected according to the actual needs, a complete image acquisition platform of bearing inner ring is built, and the software function design is completed. The simulation results show that the qualified rate of machine detection is 98.6%, the missed detection rate is 0, and the false detection rate is 1.4%, which are better than manual detection. Conclusion. The test results show that the bearing inner ring defect detection system can detect the surface defects of bearing inner ring quickly, stably, and reliably, and the detection efficiency and accuracy are higher than the original manual detection method, so it has a good application prospect.

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Section: Image Acquisition Modulementioning

confidence: 99%

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Zhu

2022

Journal of Sensors

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“…The dataset dimensions vary from 45 images [48] to more than 100,000 images [81,94,183,231], with the average number of images at 14,614 and a median of 1952. Figure 7 visualizes the number of datasets grouped by the decimal power of their dimension, ranging from less than 100 to less than 1,000,000.…”

Section: Datasets and Learning Paradigmsmentioning

confidence: 99%

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten,

Alves Gomes,

Hölken

et al. 2024

ASI

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Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

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“…Classical IDSs are not perfect because of false alarms [21]. Traditional approaches such as supervised and unsupervised machine learning [22][23][24][25][26], as well as newer technologies [27,28], have been examined for intrusion detection in the IoT. They have been analyzed and their outcomes discussed, along with every selected work objective and methodology.…”

Section: Related Workmentioning

confidence: 99%

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

et al. 2022

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The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%).

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Change Detection: The Framework of Visual Inspection System for Railway Plug Defects

Cited by 14 publications

References 31 publications

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

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