Research

21 pages, 5978 KiB

Open AccessArticle

A Road Defect Detection System Using Smartphones

by Gyulim Kim and Seungku Kim

Sensors 2024, 24(7), 2099; https://doi.org/10.3390/s24072099 - 25 Mar 2024

Viewed by 459

We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to [...] Read more.

We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to collect and label data for road defect detection research, significantly facilitating the execution of investigations in this research field. By leveraging the automatically collected data, we designed a CNN-based model to classify speed bumps, manholes, and potholes, which outperforms conventional models in both accuracy and processing speed. The proposed system represents a highly practical and scalable technology that can be implemented using commercial smartphones, thereby presenting substantial promise for real-world applications. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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17 pages, 2047 KiB

Open AccessArticle

GLFNet: Combining Global and Local Information in Vehicle Re-Recognition

by Yinghan Yang, Peng Liu, Junran Huang and Hongfei Song

Sensors 2024, 24(2), 616; https://doi.org/10.3390/s24020616 - 18 Jan 2024

Viewed by 719

Abstract

Vehicle re-identification holds great significance for intelligent transportation and public safety. Extracting vehicle recognition information from multi-view vehicle images has become one of the challenging problems in the field of vehicle recognition. Most recent methods employ a single network extraction structure, either a [...] Read more.

Vehicle re-identification holds great significance for intelligent transportation and public safety. Extracting vehicle recognition information from multi-view vehicle images has become one of the challenging problems in the field of vehicle recognition. Most recent methods employ a single network extraction structure, either a single global or local measure. However, for vehicle images with high intra-class variance and low inter-class variance, exploring globally invariant features and discriminative local details is necessary. In this paper, we propose a Feature Fusion Network (GLFNet) that combines global and local information. It utilizes global features to enhance the differences between vehicles and employs local features to compactly represent vehicles of the same type. This enables the model to learn features with a large inter-class distance and small intra-class distance, significantly improving the model’s generalization ability. Experiments show that the proposed method is competitive with other advanced algorithms on three mainstream road traffic surveillance vehicle re-identification benchmark datasets. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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15 pages, 3425 KiB

Open AccessArticle

Multivariable Coupled System Control Method Based on Deep Reinforcement Learning

by Jin Xu, Han Li and Qingxin Zhang

Sensors 2023, 23(21), 8679; https://doi.org/10.3390/s23218679 - 24 Oct 2023

Viewed by 727

Abstract

Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. [...] Read more.

Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller’s performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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20 pages, 2883 KiB

Open AccessArticle

Designing and Developing a Vision-Based System to Investigate the Emotional Effects of News on Short Sleep at Noon: An Experimental Case Study

by Ata Jahangir Moshayedi, Nafiz Md Imtiaz Uddin, Amir Sohail Khan, Jianxiong Zhu and Mehran Emadi Andani

Sensors 2023, 23(20), 8422; https://doi.org/10.3390/s23208422 - 12 Oct 2023

Cited by 4 | Viewed by 1449

Abstract

Background: Sleep is a critical factor in maintaining good health, and its impact on various diseases has been recognized by scientists. Understanding sleep patterns and quality is crucial for investigating sleep-related disorders and their potential links to health conditions. The development of non-intrusive [...] Read more.

Background: Sleep is a critical factor in maintaining good health, and its impact on various diseases has been recognized by scientists. Understanding sleep patterns and quality is crucial for investigating sleep-related disorders and their potential links to health conditions. The development of non-intrusive and contactless methods for analyzing sleep data is essential for accurate diagnosis and treatment. Methods: A novel system called the sleep visual analyzer (VSleep) was designed to analyze sleep movements and generate reports based on changes in body position angles. The system utilized camera data without requiring any physical contact with the body. A Python graphical user interface (GUI) section was developed to analyze body movements during sleep and present the data in an Excel format. To evaluate the effectiveness of the VSleep system, a case study was conducted. The participants’ movements during daytime naps were recorded. The study also examined the impact of different types of news (positive, neutral, and negative) on sleep patterns. Results: The system successfully detected and recorded various angles formed by participants’ bodies, providing detailed information about their sleep patterns. The results revealed distinct effects based on the news category, highlighting the potential impact of external factors on sleep quality and behaviors. Conclusions: The sleep visual analyzer (VSleep) demonstrated its efficacy in analyzing sleep-related data without the need for accessories. The VSleep system holds great potential for diagnosing and investigating sleep-related disorders. The proposed system is affordable, easy to use, portable, and a mobile application can be developed to perform the experiment and prepare the results. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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16 pages, 1319 KiB

Open AccessArticle

Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs

by Liang Zou, Shifan Xu, Weiming Zhu, Xiu Huang, Zihui Lei and Kun He

Sensors 2023, 23(16), 7296; https://doi.org/10.3390/s23167296 - 21 Aug 2023

Cited by 1 | Viewed by 1056

Abstract

Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show clear details. In [...] Read more.

Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show clear details. In this study, we introduce a novel Generative Adversarial Network (GAN) to restore high-definition coal photomicrographs. Compared to traditional image restoration methods, the lightweight GAN-based network generates more explicit and realistic results. In particular, we employ the Wide Residual Block to eliminate the influence of artifacts and improve non-linear fitting ability. Moreover, we adopt a multi-scale attention block embedded in the generator network to capture long-range feature correlations across multiple scales. Experimental results on 468 photomicrographs demonstrate that the proposed method achieves a peak signal-to-noise ratio of 31.12 dB and a structural similarity index of 0.906, significantly higher than state-of-the-art super-resolution reconstruction approaches. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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19 pages, 985 KiB

Open AccessArticle

Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural Network

by Dalila Say, Salah Zidi, Saeed Mian Qaisar and Moez Krichen

Sensors 2023, 23(14), 6422; https://doi.org/10.3390/s23146422 - 14 Jul 2023

Cited by 6 | Viewed by 2965

Abstract

The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack [...] Read more.

The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration. In this context, this study proposes an automated approach to identify multi-class welding defects by processing the X-ray images. It is realized by an intelligent hybridization of the data augmentation techniques and convolutional neural network (CNN). The proposed data augmentation mainly performs random rotation, shearing, zooming, brightness adjustment, and horizontal flips on the intended images. This augmentation is beneficial for the realization of a generalized trained CNN model, which can process the multi-class dataset for the identification of welding defects. The effectiveness of the proposed method is confirmed by testing its performance in processing an industrial dataset. The intended dataset contains 4479 X-ray images and belongs to six groups: cavity, cracks, inclusion slag, lack of fusion, shape defects, and normal defects. The devised technique achieved an average accuracy of 92%. This indicates that the approach is promising and can be used in contemporary solutions for the automated detection and categorization of welding defects. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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25 pages, 6962 KiB

Open AccessArticle

A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors

by Bingxin Luo, Ziming Kou, Cong Han, Juan Wu and Shaowei Liu

Sensors 2023, 23(14), 6276; https://doi.org/10.3390/s23146276 - 10 Jul 2023

Cited by 5 | Viewed by 1735

Abstract

Coal flow in belt conveyors is often mixed with foreign objects, such as anchor rods, angle irons, wooden bars, gangue, and large coal chunks, leading to belt tearing, blockages at transfer points, or even belt breakage. Fast and effective detection of these foreign [...] Read more.

Coal flow in belt conveyors is often mixed with foreign objects, such as anchor rods, angle irons, wooden bars, gangue, and large coal chunks, leading to belt tearing, blockages at transfer points, or even belt breakage. Fast and effective detection of these foreign objects is vital to ensure belt conveyors’ safe and smooth operation. This paper proposes an improved YOLOv5-based method for rapid and low-parameter detection and recognition of non-coal foreign objects. Firstly, a new dataset containing foreign objects on conveyor belts is established for training and testing. Considering the high-speed operation of belt conveyors and the increased demands for inspection robot data collection frequency and real-time algorithm processing, this study employs a dark channel dehazing method to preprocess the raw data collected by the inspection robot in harsh mining environments, thus enhancing image clarity. Subsequently, improvements are made to the backbone and neck of YOLOv5 to achieve a deep lightweight object detection network that ensures detection speed and accuracy. The experimental results demonstrate that the improved model achieves a detection accuracy of 94.9% on the proposed foreign object dataset. Compared to YOLOv5s, the model parameters, inference time, and computational load are reduced by 43.1%, 54.1%, and 43.6%, respectively, while the detection accuracy is improved by 2.5%. These findings are significant for enhancing the detection speed of foreign object recognition and facilitating its application in edge computing devices, thus ensuring belt conveyors’ safe and efficient operation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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15 pages, 5916 KiB

Open AccessArticle

Two-Stream Network One-Class Classification Model for Defect Inspections

by Seunghun Lee, Chenglong Luo, Sungkwan Lee and Hoeryong Jung

Sensors 2023, 23(12), 5768; https://doi.org/10.3390/s23125768 - 20 Jun 2023

Viewed by 994

Abstract

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect [...] Read more.

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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34 pages, 7465 KiB

Open AccessArticle

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

by Victor Carvalho Galvão De Freitas, Valbério Gonzaga De Araujo, Daniel Carlos de Carvalho Crisóstomo, Gustavo Fernandes De Lima, Adrião Duarte Dória Neto and Andrés Ortiz Salazar

Sensors 2022, 22(23), 9162; https://doi.org/10.3390/s22239162 - 25 Nov 2022

Cited by 2 | Viewed by 2583

Abstract

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly [...] Read more.

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology)

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