Development of a measuring system for the diameter of a smooth plug gauge based on machine vision technology

Feng, Laijian; Tang, Diankui; Chen, Manlong

doi:10.1177/00202940221098070

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The advantages are clearer image edges, better continuity, and improved thread detection accuracy. Feng et al 20 designed a smooth plug gauge diameter measurement system based on machine vision technology. The images are localized and segmented using a threshold neighborhood approach.…”

Section: Introductionmentioning

confidence: 99%

Compensation algorithm for taper thread distortion with large pitch and biased trapezoid

Qianhai,

Lingfei,

Longlong

et al. 2023

Opt. Eng.

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We propose a taper thread compensation algorithm that considers the taper thread tilt state and tooth distortion to address the problem of low accuracy of taper thread inspection by machine vision. The analysis of the taper thread inspection process reveals the reason for the low accuracy of taper thread inspection, and the mathematical modeling of the taper thread is established according to the spatial geometric relationship. The experiment shows that the compensation rate of the algorithm is 80% of the base median, 70% of the thread half-angle, and 85% of the bottom width of the thread compared with that before the compensation, and it has less influence on the large diameter and pitch.

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Section: Introductionmentioning

confidence: 99%

Compensation algorithm for taper thread distortion with large pitch and biased trapezoid

Qianhai,

Lingfei,

Longlong

et al. 2023

Opt. Eng.

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show abstract

“…In the context of intelligent manufacturing, machine vision [1,2] has become an important research direction for artificial intelligence, and same-target defect detection has become the focus of research. In recent years, with the rapid development of China's industrial level, the demand for metallurgical saw blades has increased significantly.…”

Section: Introductionmentioning

confidence: 99%

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng,

Cui,

Liu

et al. 2023

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Under the background of intelligent manufacturing, in order to solve the complex problems of manual detection of metallurgical saw blade defects in enterprises, such as real-time detection, false detection, and the detection model being too large to deploy, a study on a metallurgical saw blade surface defect detection algorithm based on SC-YOLOv5 is proposed. Firstly, the SC network is built by integrating coordinate attention (CA) into the Shufflenet-V2 network, and the backbone network of YOLOv5 is replaced by the SC network to improve detection accuracy. Then, the SIOU loss function is used in the YOLOv5 prediction layer to solve the angle problem between the prediction frame and the real frame. Finally, in order to ensure both accuracy and speed, lightweight convolution (GSConv) is used to replace the ordinary convolution module. The experimental results show that the mAP@0.5 of the improved YOLOv5 model is 88.5%, and the parameter is 31.1M. Compared with the original YOLOv5 model, the calculation amount is reduced by 56.36%, and the map value is increased by 0.021. In addition, the overall performance of the improved SC-YOLOv5 model is better than that of the SSD and YOLOv3 target detection models. This method not only ensures the high detection rate of the model, but also significantly reduces the complexity of the model and the amount of parameter calculation. It meets the needs of deploying mobile terminals and provides an effective reference direction for applications in enterprises.

show abstract

Development of a measuring system for the diameter of a smooth plug gauge based on machine vision technology

Cited by 2 publications

References 19 publications

Compensation algorithm for taper thread distortion with large pitch and biased trapezoid

Compensation algorithm for taper thread distortion with large pitch and biased trapezoid

Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

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