Visual detection of milling surface roughness based on improved YOLOV5

• Autorzy: Lv Xiao , Yi Huaian , Fang Runji , Ai Shuhua , Lu Enhui

Identyfikatory

DOI

10.24425/mms.2023.146425

• Języki publikacji: EN

Abstrakty

EN

Workpiece surface roughness measurement based on traditional machine vision technology faces numerous problems such as complex index design, poor robustness of the lighting environment, and slow detection speed, which make it unsuitable for industrial production. To address these problems, this paper proposes an improved YOLOv5 method for milling surface roughness detection. This method can automatically extract image features and possesses higher robustness in lighting environments and faster detection speed. We have effectively improved the detection accuracy of the model for workpieces located at different positions by introducing Coordinate Attention (CA). The experimental results demonstrate that this study’s improved model achieves accurate surface roughness detection for moving workpieces in an environment with light intensity ranging from 592 to 1060 lux. The average precision of the model on the test set reaches 97.3%, and the detection speed reaches 36 frames per second.

Słowa kluczowe

EN

surface roughness; improved Yolov5; detection speed; attentional mechanisms

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2023
• Tom: Vol. 30, nr 3
• Strony: 531--548
• Opis fizyczny: Bibliogr. 22 poz., fot., rys., tab., wykr., wzory

Twórcy

autor

Lv Xiao

• School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China

autor

Yi Huaian

• School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China

autor

Fang Runji

• School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China

autor

Ai Shuhua

• School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, People’s Republic of China

autor

Lu Enhui

• School of Mechanical Engineering, Yangzhou University, Yangzhou, 225009, People’s Republic of China

Bibliografia

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Uwagi

1. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 52065016).

2. Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).