Weld-pool image centroid algorithm for seam-tracking vision model in arc-welding process

Gao, Xiang; Ding, Dukun; Bai, Tianxiang; Katayama, Seiji

doi:10.1049/iet-ipr.2009.0231

Cited by 40 publications

(14 citation statements)

References 12 publications

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“…With the development of science and technology, welding technology has been applied to various fields, such as the automobile industry, machinery manufacturing, the shipbuilding industry, aerospace, biomedicine, powder metallurgy, the microelectronics industry, energy generation, and other fields [1][2][3]. In the welding process, due to the influence of the weldment surface condition, joint gap [4], welding power [5], welding speed [6], and other factors, welded products are prone to welding defects, which directly affect the products' quality [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Gao

Tian

et al. 2022

Metals

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Using the traditional magneto-optical detection methods, micro-weld defects parallel with the magnetic field direction may be overlooked. In order to overcome this, a non-destructive testing method based on magneto-optical imaging under a vertical combined magnetic field (VCMF) is proposed. To demonstrate this, the experimental results of the magneto-optical imaging of weld defects excited by a vertical combined magnetic field (VCMF) or parallel combined magnetic field (PCMF) are compared with those of traditional magnetic fields (constant magnetic field (CMF), alternating magnetic field (AMF), and rotating magnetic field (RMF)). It is found that the magneto-optical imaging under a VCMF can accurately detect weld defects of any shape and distribution. In addition, the center difference method is used to eliminate the influence of noise on the defect contour extraction of magneto-optical images, and the active contour of weld defects in the magneto-optical images is extracted. The results show that many noises can be identifiedby the robustness of the level set method, operating in low-pass filtering, so that much information that is usually lost can be retained.

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Section: Introductionmentioning

confidence: 99%

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Gao

Tian

et al. 2022

Metals

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“…At present, surface defects detection methods mainly include traditional image processing methods and deep learning methods [1][2][3]. Traditional image processing methods detect targets through edge detection, threshold segmentation, feature histogram, classical machine learning methods [2,[4][5][6][7][8][9][10] (support vector machine, k-Nearest Neighbor method and Naive Bayes, neural network, decision tree, etc.). Literature [11] constructed neural network to detect welding defects under alternating/rotating magnetic field, and the test detection accuracy is 94.1%.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

2022

Teh. vjesn.

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The surface defects of automobile door seals are mainly detected manually at present, which is costly and has low efficiency. Therefore, a deep learning automatic detection algorithm of automobile door seals is studied in this paper. At first, the defects are classified and the data set is made according to the geometric characteristics of the defects. While enhancing the data set, the K-means clustering algorithm is used to cluster the target annotation frame in the data set, and the anchor that matches with the surface defect size of the seal is obtained. Finally, in view of the characteristics of large variation range of defect size, the target detection algorithm YOLOV3 is selected as the basic framework. Meanwhile, considering the high proportion of small and medium-sized targets in defects, the output scale is introduced at 4 times of the sampling position of YOLOV3 backbone network. In order to further enhance the correlation between the extracted features and the channel, spatial position and coordinate position, the feature fusion and attention mechanism module is constructed. The test results show that the mean of the average precision is improved by 4.81% compared with YOLOV3.

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“…However, long-term and focused observation would cause visual fatigue and work inertia of security personnel, which increase the false detection rate and missed detection rate, resulting in security risks. Nowadays, deep learning technology has exhibited excellent performance in many fields [1][2][3][4][5], and the application of object detection technology in the detection of contraband has become one of the research hotspots.…”

Section: Introductionmentioning

confidence: 99%

Detection of Prohibited Items Based upon X-ray Images and Improved YOLOv7

Yuan

Zhang

Xie

et al. 2022

J. Phys.: Conf. Ser.

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Safety inspection nowadays is an effective means to safeguard public security, which mainly relies on professional security personnel to carry out inspections. In order to detect automatically contraband in X-ray images, a new prohibited item detection method on the strength of the modified YOLOv7 algorithm is present. The spatial attention constructed by large kernel attention was introduced into the lower layer of the YOLOv7 backbone network to extract the remote dependence information and texture information of the lower layer feature map. The proposed method was tested on public X-ray data set for a safety inspection, and the result showed that the improved means can advance the detection accuracy of the model.

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Weld-pool image centroid algorithm for seam-tracking vision model in arc-welding process

Cited by 40 publications

References 12 publications

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Magneto-Optical Imaging of Arbitrarily Distributed Defects in Welds under Combined Magnetic Field

Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

Detection of Prohibited Items Based upon X-ray Images and Improved YOLOv7

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