Laser stripe extraction in additive manufacturing based on spatiotemporal noise regularization

Yu, Haotian; Peng, Chongchong; Zhao, Zhuang; Zhang, Yi

doi:10.1007/s10043-020-00623-7

Cited by 1 publication

(1 citation statement)

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“…In addition to locating the welding points, the actual welding process also requires fitting the welding planes to adjust the position of the welding robot in real time, 9,10 so the region of interest needs to be extracted from the laser stripe images. 11 Yu et al 12 extracted the coordinates of pixels with a grayscale value of 255 and calculated their maximum bounding rectangle as the laser strip region. Bo et al 13 recorded the pixels with a maximum grayscale value of non-zero and determined the boundaries of the laser strip region.…”

Section: Introductionmentioning

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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