Method for Extracting the Centerline of Line Structured Light Based on Quadratic Smoothing Algorithm

Yi, 杨毅 Yang; Bing, 闫兵 Yan; Dawei, 董大伟 Dong; Yan, 黄燕 Huang; Zhongzhi, 唐忠智 Tang

doi:10.3788/lop57.101504

Cited by 2 publications

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“…Therefore, the feature extraction of weld laser fringe images is the most critical content in the research of quality parameter measurement. Laser image feature extraction mainly includes two parts: centerline extraction [48] and feature point extraction [49]. The centerline extraction methods mainly include the gray centroid method, curve fitting method, morphological refinement method, Steger algorithm, and so on.…”

Section: Discussionmentioning

confidence: 99%

A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

et al. 2022

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Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

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Section: Discussionmentioning

confidence: 99%

A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

et al. 2022

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基于单边跟踪与中点预测的线结构光中心提取算法研究

Wang Rongjun,

Huang Mingming,

Ma lidong

2024

中国激光

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Objective Linestructured light measurement technology is widely used in the field of industrial inspection. In the measurement system, the center extraction accuracy of the laser stripe directly influences the final measurement accuracy. Unfortunately, some factors in the industrial environment significantly affect the center extraction of the laser stripe. For example, the rounded convexity on the surface of a detected object leads to a large change in the curvature of the collected laser stripe, and the reflections on the surface of the detected object lead to a laser stripe connected with reflective noise, resulting in severe interference. In these situations, existing algorithms, such as the geometric center, gray gravity, and Steger algorithms, cannot obtain proper extraction results. In case of a large curvature change, existing algorithms fail to accurately extract the center of the laser stripe along its normal direction, resulting in a large deviation from the actual center. In the case of severe interference, existing algorithms fail to efficiently avoid interference disturbances; thus, the interference is calculated as part of the laser stripe. Therefore, it is necessary to implement countermeasures to solve these problems and improve the applicability of the center extraction algorithm .Conclusions This study proposes a centerextraction algorithm for linestructured light based on unilateral tracking and midpoint prediction, which proves that the proposed algorithm can deal with laser stripe under the conditions with large curvature change or serious interference and obtain proper results. In addition, the proposed algorithm is faster and more applicable than existing algorithms. These advantages enable the proposed algorithm to provide support for linestructured light measurements under nonideal conditions.

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Method for Extracting the Centerline of Line Structured Light Based on Quadratic Smoothing Algorithm

Cited by 2 publications

References 0 publications

A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

基于单边跟踪与中点预测的线结构光中心提取算法研究

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