A Measurement System for the Tightness of Sealed Vessels Based on Machine Vision Using Deep Learning Algorithm

Ding, Zhenglong; Song, Wan; Zhan, Shu

doi:10.1109/tim.2022.3158989

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…The results showed that the mAP reached 94.3%, and bubbles can be accurately identified with relatively high confidence. Ding et al [31] designed a dry leakage measurement framework for sealed container based on deep learning. The YOLOv5 model with asymmetric convolutional blocks in the backbone was used for bubble detection in tightness measurement, making the features of small targets easier to extract.…”

Section: Deep Learning Based Detection Methodsmentioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

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Bubble detection has important applications in industries such as tightness testing, fluid measurement, healthcare, and chemical engineering. Due to the fact that bubbles belong to small targets with characteristics such as reflection and shadows, they have fewer available features and are highly susceptible to environmental interference, which makes it difficult for detection models to accurately locate and recognize the bubbles. Aiming at these problems, an improved YOLOv8n for bubble detection is proposed. Firstly, the deformable convolution network (DCN) is used in the Backbone module to take place of the original C2f module, which enables the model to have stronger feature extraction and adaptive generalization capabilities for small targets with different deformations. Then the global attention mechanism (GAM) is introduced into Neck network to locally enhance the channels or regions of interest, which is beneficial for capturing the important features of targets, especially small targets like bubbles. Finally, the loss function is improved so that boundary box regression and target detection become more accurate. Experiments are conducted on the bubble dataset, and the results indicate the mean average precision (mAP) of the improved algorithm reaches 97.4%, increasing by 2.2% compared to the original YOLOv8n.This shows the proposed method has better comprehensive performance on bubble detection.

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Section: Deep Learning Based Detection Methodsmentioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

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“…In addition, our models were evaluated using Jaccard loss, also known as the Intersection over Union (IoU) metric, a standard measure for assessing segmentation performance [56,57]. As part of our experimental design, we computed the This article has been accepted for publication in IEEE Access.…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection

Dashdondov,

Kim,

2023

IEEE Access

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Natural gas is widely used for domestic and industrial purposes, and whether it is being leaked into the air cannot be directly known. The current problem is that gas leakage is not only economically harmful but also detrimental to health. Therefore, much research has been done on gas damage and leakage risks, but research on predicting gas leakages is just beginning. In this study, we propose a method based on deep learning to predict gas leakage from environmental data. Our proposed method has successfully improved the performance of machine learning classification algorithms by efficiently preparing training data using a deep autoencoder model. The proposed method was evaluated on an open dataset containing natural gas and environmental information and compared with extreme gradient boost (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), and naive Bayes (NB) algorithms. The proposed method is evaluated using accuracy, F1-score, mean square error (MSE), mean intersection over union (mIoU), and area under the ROC curve (AUC). The presented method in this study outperformed all compared methods. Moreover, the deep autoencoder and ordinal encoder-based XGBoost (DA-MA-XGBoost) showed the best performance by giving 99.51% accuracy, an F1-score of 99.53%, an MSE of 0.003, mIoU of 99.40 and an AUC of 99.62%.INDEX TERMS Deep autoencoder, multivariate analysis, methane gas, risk detection, XGBoost.

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Synchronous Imaging and Multimodal Fusion of Optical and Electromagnetic Measurements for Overlapping Defects Inspection

Zhang

Dong

2024

IEEE Trans. Ind. Inf.

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A Measurement System for the Tightness of Sealed Vessels Based on Machine Vision Using Deep Learning Algorithm

Cited by 6 publications

References 38 publications

Research on Bubble Detection Based on Improved YOLOv8n

Research on Bubble Detection Based on Improved YOLOv8n

NDAMA: A Novel Deep Autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection

Synchronous Imaging and Multimodal Fusion of Optical and Electromagnetic Measurements for Overlapping Defects Inspection

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