An adaptive defect detection method for LNG storage tank insulation layer based on visual saliency

Liu, Huizhou; Hu, Jinqiu

doi:10.1016/j.psep.2021.10.041

Cited by 6 publications

(3 citation statements)

References 54 publications

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“…This method is simple to implement and computationally efficient. Liu et al [18] addressed the challenge of significant temperature differences between defect areas and other regions by proposing an adaptive detection method that utilizes infrared thermography and saliency detection algorithms. Vivekananthan.…”

Section: Traditional Image Processing Methodsmentioning

confidence: 99%

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Yang,

Zhou,

Wang

et al. 2023

Preprint

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Some common problems, including the effect of non-detection regions on accuracy, the small size and multi-scale of defects,and the challenge of automatically optimizing neural network hyperparameters, are confronted during the metal gear end-face defect detection, lead to the inadequate performance of accuracy and efficiency, making them unsuitable for meeting the real-time online detection demands in industries. To address the problems above, this study proposes a method SF-YOLONet to detect defects on metal gear end faces by using the Optimized Evolutionary Algorithm. Firstly, a testing platform was constructed to detect surface defects on metal gear end-faces. Subsequently, in order to address the impact of non-detection regions on accuracy, this study introduces the SF algorithm, a visual saliency-based image extraction method, to eliminates interference between ineffective features in non-detection regions and edge burrs. Additionally, A network (YOLONet) for detecting end-face defects in metal gears is introduced, which integrates the CBAM module and BiFPN feature extraction strategy. These advancements enhance adaptive learning and feature extraction for small-sized defects on gear end-face, and combine low-resolution and deep-level semantic information, to detect small and multi-scale defects is enhanced. Finally, the ISSA algorithm is introduced to optimize the hyperparameters of the SF-YOLONet model, thereby avoiding the instability of manual parameterization. The experiment demonstrated that the SF-YOLONet model achieved an average precision of 98.01% and an F1 score of 0.99 on the Metal Gear end-face defect testing dataset. The average computation time for detection per image on the YOLONet model was 0.13 seconds. Compared to other deep learning models, the proposed SF-YOLONet model significantly improves precision and efficiency in detecting defects on gear end-face. This enhancement effectively meets the real-time online detection requirements of industries.

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Section: Traditional Image Processing Methodsmentioning

confidence: 99%

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Yang,

Zhou,

Wang

et al. 2023

Preprint

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“…This method can reduce noise impact and improve the quality of infrared image acquisition. Infrared images can effectively reflect the abnormal situation of device after segmentation [13]. In relevant research, filtering algorithms can significantly reduce noise impact after being weighted.…”

Section: Related Workmentioning

confidence: 99%

“…In formula (13), d represents the prior box position, and variance is hyperparameter, and g  can be scaled. Formula ( 14) is the encoding method for width and height.…”

mentioning

confidence: 99%

Intelligent Detection System for Electrical Equipment based on Deep Learning and Infrared Image Processing Technology

Lu,

Xie

2023

IJACSA

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The demand for the reliability of power grid systems is gradually increasing with the development of the power industry. And it is necessary to promptly identify and eliminate the hidden dangers. To meet the needs of online monitoring and the early warning of electrical equipment, an intelligent detection system based on deep learning and infrared image processing technology is proposed in this study. Firstly, the infrared image is preprocessed for noise reduction. Then, an improved SSD (Single Shot MultiBox Detector) network is used to optimize the infrared image detection method. Based on this, an intelligent detection system for electrical equipment is designed. The results show that the mAP value of the improved SSD network after 1200 iterations is about 92.58%, and its area under the Precision Recall (PR) curve is higher than other algorithms. The simulation analysis results of the detection system show that the improved method detects a fault degree of 57.85%, which is closer to the 59.74% in the real situation. The experimental results indicate that the newly established intelligent detection system for electrical equipment can effectively detect its abnormal situations.

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Research on digital twin based temperature field monitoring system for LNG storage tanks

Sun

et al. 2023

Measurement

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An adaptive defect detection method for LNG storage tank insulation layer based on visual saliency

Cited by 6 publications

References 54 publications

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Intelligent Detection System for Electrical Equipment based on Deep Learning and Infrared Image Processing Technology

Research on digital twin based temperature field monitoring system for LNG storage tanks

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