A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing

Zhao, Congyang; Yang, Jaeyoung; Zhou, Fuqiang; Sun, Junhua; Li, Xiaosong; Xie, Wenjie

doi:10.3390/s20164544

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…To ensure the imaging effect, it is necessary to project the laser stripe to the center of the photosensitive plane as much as possible. Zhao et al 24 proposed a formula to calculate the error of visual sensor detection about the angle φ and distance d. Under the premise of ensuring the imaging effect of the camera, it was estimated that the sensor detection error is more minor when φ ¼ 76.49 deg and d ¼ 92.6 mm, and the laser stripe is clear and located in the center of the image. The schematic diagram of the vision sensor used in this paper is shown in Fig.…”

Section: Principle Of Structured Light Vision Sensormentioning

confidence: 99%

Improved YOLO-based laser stripe region extraction method for welding robots

Song,

Wang,

et al. 2024

J. Electron. Imag.

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Real-time image processing and regions of interest extraction are crucial in the nonstandard welding process guided by structured light vision. However, due to the impact of detection speed, accuracy, and applicability, existing methods are difficult to apply directly. To address these issues, we have screened various improvement methods through experiments and provided a practical lightweight algorithm, YOLO-DGB, based on YOLOv5s, a current mainstream object detection algorithm. The proposed algorithm introduces depthwise separable convolution and Ghost modules into the backbone network to reduce the number of parameters and floating-point operations per second (FLOPs) in the detection process. To meet the accuracy requirements of the detection network, a bottleneck transformer is introduced after the spatial pyramid pooling fast, which improves the detection effect while ensuring a reasonable number of parameters and computation. To address the issue of insufficient datasets, we propose an improved DCGAN network to enhance the collected images. Compared with the original YOLOv5s network, our proposed algorithm reduces the number of parameters and FLOPs by 35.5% and 44.8%, respectively, while increasing the mAP of the model from 96.5% to 98.1%. Experimental results demonstrate that our algorithm can effectively meet the requirements of actual production processes.

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Section: Principle Of Structured Light Vision Sensormentioning

confidence: 99%

Improved YOLO-based laser stripe region extraction method for welding robots

Song,

Wang,

et al. 2024

J. Electron. Imag.

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“…The gray barycentric algorithm [ 28 ] was utilized to quickly and efficiently extract the stripe centerline. The “centroid” in the pixel area was considered the pixel center in the area, and the calculation formula of the area centroid is expressed as follows:

where

represents the gray value of the pixel point with coordinates ( c , r ), Ω denotes the set of target regions, and (

) is the gray barycentric coordinate of the region.…”

Section: Laser Stripe Feature Points Extractionmentioning

confidence: 99%

Research on Trajectory Recognition and Control Technology of Real-Time Tracking Welding

Zhao

Zhang

Wang

et al. 2022

Sensors

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Real-time tracking welding with the assistance of structured light vision enhances the intelligence of robotic welding, which significantly shortens teaching time and guarantees accuracy for user-customized product welding. However, the robustness of most image processing algorithms is deficient during welding practice, and the security regime for tracking welding is not considered in most trajectory recognition and control algorithms. For these two problems, an adaptive feature extraction algorithm was proposed, which can accurately extract the seam center from the continuous, discontinuous or fluctuating laser stripes identified and located by the CNN model, while the prior model can quickly remove a large amount of noise and interference except the stripes, greatly improving the extraction accuracy and processing speed of the algorithm. Additionally, the embedded Pauta criterion was used to segmentally process the center point data stream and to cyclically eliminate outliers and further ensure the accuracy of the welding reference point. Experimental results showed that under the guarantee of the above-mentioned seam center point extraction and correction algorithms, the tracking average error was 0.1 mm, and even if abnormal trajectory points existed, they did not cause welding torch shaking, system interruption or other accidents.

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“…Recently, using deep learning techniques to extend the performance of traditional algorithms is becoming popular among researchers. Liu et al [2], Zhao et al [3], and Yu et al [4] proposed neural networks for the noise reduction process before centerline extraction, respectively. Learning-based methods perform automatic laser stripe region detection and segmentation by learning the distribution properties of the noise from a large data set, which has improved the performance of subsequent algorithms for extracting the centerline.…”

Section: Introductionmentioning

confidence: 99%

A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction

Huang

2023

J. Phys.: Conf. Ser.

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To overcome the stray light noise in the centerline extraction method during line structured light 3D reconstruction process, an end-to-end trainable neural network for laser stripe centerline extraction based on Convolutional Neural Network and Multi-Layer Perception is proposed. The proposed network can self-adapt to a variety of lighting (brightness) conditions and overcome the interference of different stray lights. In addition, unlike prior work on enhancing the accuracy of centerline extraction using deep learning methods that only performs it for noise reduction in pre-processing, the proposed network unifies the noise reduction and prediction processes, so that it can be optimized end-to-end directly on centerline extraction performance. The network learns an intermediate feature representation of noise reduction, which requires less complexity for data annotation, reduces the training difficulty, and has more scalability. Experiments show that the proposed method can perform centerline extraction with relatively high accuracy for laser stripes of different widths, brightness, and inclination, thus obtaining a smooth and stable reconstructed surface in the structured light 3D reconstruction process.

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A Robust Laser Stripe Extraction Method for Structured-Light Vision Sensing

Cited by 16 publications

References 37 publications

Improved YOLO-based laser stripe region extraction method for welding robots

Improved YOLO-based laser stripe region extraction method for welding robots

Research on Trajectory Recognition and Control Technology of Real-Time Tracking Welding

A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction

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