Deep Learning Based Steel Pipe Weld Defect Detection

Yang, Dingming; Cui, Yanrong; Yu, Zeyu; Yuan, Hongqiang

doi:10.1080/08839514.2021.1975391

Cited by 75 publications

(33 citation statements)

References 6 publications

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“…To understand the behavior of the AI model, model interpretation tools such as Heat maps and Grad CAM tools are used. A U-Net architecture-based model is presented in [13], a method based on YOLOv5 model demonstrated in [14] which uses X-Ray images for detection of pipeline weld defects, the results indicated that YOLOv5 performs better than R-CNN interns of metrics such as speed of detection and classification accuracy. The detection of cracks which are thin, irregular in shape and with complex texture background in the image makes crack detection task challenging.…”

Section: Pretrained Models For Pipeline Health Detectionmentioning

confidence: 99%

Industrial Fluids Components Health Management Using Deep Learning

Biradar¹,

Nagaraj²,

Mohan³

et al. 2023

Artificial Intelligence

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The fatigue state of fluid components such as valves, metal surfaces in gas or oil carrying pipelines is important to monitor on regular basis and plan for repair work to avoid risks associated with them, this becomes more crucial when the pipelines are supplying hazard prone fluids. There exist methods for detection of corroded surfaces, scratches and fractures in pipelines, valves, and regulators etcetera. The conventional methods are based on sensors and chemical analysis methods. There are challenges with conventional methods pertaining to the desired metric of scalability and disadvantages of these methods is they are contact based and destructive methods. Therefore, to overcome these limitations of existing methods there is a need for development of non-contact and nondestructive methods. The recent advancements in Artificial Intelligence technology in every domain including health care monitoring, agriculture sector, defense applications and civilian applications etc., have shown that deep learning methods can be explored in industrial applications to develop fault tolerant systems which help fluid components state of health monitoring through computer vision. In this chapter proposes various methods for analysis of health state of fluid components using deep convolutional neural networks and suggest the best models for these applications.

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Section: Pretrained Models For Pipeline Health Detectionmentioning

confidence: 99%

Industrial Fluids Components Health Management Using Deep Learning

Biradar¹,

Nagaraj²,

Mohan³

et al. 2023

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Feng [16] added FcaNet (Frequency Channel Attention Networks) and CBAM (Convolutional Block Attention Module) on the basis of Res-Net, the dataset contained 1360 images, and the parameter quantity was 26.038 M. The dataset was small, and the model was large. Yang [17] used YOLOV5X (You Only Look Once) to detect steel pipe welding defects. The accuracy was 98.7, but the inference speed was 120 ms, which did not meet the real-time industrial requirements.…”

Section: Overall Framework and Algorithm Structure Of Steel Plate Def...mentioning

confidence: 99%

“…There are three filters, and each filter is a 3 × 3 convolution kernel. (1) YOLOV5 YOLOV5 [17,27,28] mainly consists of Backbone, Neck and Head. Mosaic data enhancement, adaptive anchor box calculation and adaptive image scaling were added to the data input part to process the data and increase the detection accuracy.…”

Section: Model Structurementioning

confidence: 99%

Steel Plate Surface Defect Detection Based on Dataset Enhancement and Lightweight Convolution Neural Network

et al. 2022

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In the production and manufacturing industry, factors such as rolling equipment and processes may cause various defects on the surface of the steel plate, which greatly affect the performance and subsequent machining accuracy. Therefore, it is essential to identify defects in time and improve the quality of production. An intelligent detection system was constructed, and some improved algorithms such as dataset enhancement, annotation and lightweight convolution neural network are proposed in this paper. (1) Compared with the original YOLOV5 (You Only Look Once), the precision is 0.924, and the inference time is 29.8 ms, which is 13.8 ms faster than the original model. Additionally, the parameters and calculations are also far less than YOLOV5. (2) Ablation experiments were designed to verify the effectiveness of the proposed algorithms. The overall accuracy was improved by 0.062; meanwhile, the inference time was reduced by 21.7 ms. (3) Compared with other detection models, although RetinaNet has the highest accuracy, it takes the longest time. The overall performance of the proposed method is better than other methods. This research can better meet the requirements of the industry for precision and real-time performance. It can also provide ideas for industrial detection and lay the foundation for industrial automation.

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“…Du et al [24] devised Convolutional Neural Network (CNN) to reduce the difficulty of parameter adjustment and improve the stability of the seam tracking system. Yang et al [25] realized the latest single-stage target detection algorithm YOLOv5 applied to the field of steel pipe weld defect detection, which can improve the detection accuracy of welding quality. Pan et al [26] devised a new transfer learning model based on MobileNet as a feature extraction tool for welding defects, with fast speed and prediction accuracy of 97.69%.…”

Section: Introductionmentioning

confidence: 99%

An active visual monitoring method for GMAW weld surface defects based on random forest model

Zhu

Yuan

2022

Mater. Res. Express

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In the automatic manufacturing of robotic welding, real-time monitoring of weld quality is a difficult problem. Meanwhile, due to volatilization of zinc vapor in galvanized steel and complexity of welding process, the existence of welding defects greatly affects industrial production process. And real-time detection of welding defects is a key step in development of intelligent welding. To realize real-time monitoring of weld surface defects, an active visual monitoring method for weld surface defects is proposed. Firstly, after applying Gabor filter to remove interference signals such as arc and noise, obtain weld centerline image; then employ the slope analysis method to extract peak valley coefficient of weld defects, extract five feature points of weld surface quality by Douglas-Puke algorithm, and analyse geometric and spatial distribution features of different types of defects in weld laser stripe images. Finally, using eight feature vectors extracted from weld features, design a weld defect recognition random forest model based on decision tree. After optimizing the decision tree depth and number of model evaluators, compared with the traditional decision tree ID3 and CART algorithm model, this model has better performance than traditional machine learning algorithms on five weld surface defect data sets. The experimental results show that accuracy of weld defect identification in the training set is 99.26%, accuracy of weld defect recognition in the test set is 96%, and processing time of a single image is only 5.3 ms, which overcomes difficulty of real-time weld defect detection in intelligent welding and ensures real-time and accuracy.

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Deep Learning Based Steel Pipe Weld Defect Detection

Cited by 75 publications

References 6 publications

Industrial Fluids Components Health Management Using Deep Learning

Industrial Fluids Components Health Management Using Deep Learning

Steel Plate Surface Defect Detection Based on Dataset Enhancement and Lightweight Convolution Neural Network

An active visual monitoring method for GMAW weld surface defects based on random forest model

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