- Tytuł:
- Visual detection of milling surface roughness based on improved YOLOV5
- Autorzy:
-
Lv, Xiao
Yi, Huaian
Fang, Runji
Ai, Shuhua
Lu, Enhui - Powiązania:
- https://bibliotekanauki.pl/articles/27311755.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Czasopisma i Monografie PAN
- Tematy:
-
surface roughness
improved Yolov5
detection speed
attentional mechanisms - Opis:
- 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.
- Źródło:
-
Metrology and Measurement Systems; 2023, 30, 3; 531--548
0860-8229 - Pojawia się w:
- Metrology and Measurement Systems
- Dostawca treści:
- Biblioteka Nauki
Artykuł