首页|A modified-YOLOv5s model for detection of wire braided hose defects
A modified-YOLOv5s model for detection of wire braided hose defects
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2021High-pressure steel wire braided hoses have periodic braided steel wire structure and its productivity is too huge to refrain from making mistakes occasionally such as wire missed wire stacked and wire loosened. The major sticking points in detection are ultra-small, structural similarity and high reflectance of the steel wire braided hoses. Therefore, this paper proposed an automated method based on YOLOv5 to replace traditional naked eyes detection. The anchor boxes were optimized by K-means++ algorithm for defects dataset. Focal loss was used to mitigate the impact of samples imbalance. The predict heads were modified to increase the detection accuracy of ultra-small targets with guaranteeing speed. The effect of efficient channel attention mechanism (ECA) in different positions was analyzed to achieve a better network and increase detection performance. The experimental results showed that the accuracy and recognition efficiency of the proposed modified-YOLOv5s model could reach 92.2% and 23 frames per second.
Deep learningDefect detectionMachine visionWire braided hosesYOLOv5
Ying Z.、Lin Z.、Wu Z.、Hu X.、Liang K.
展开 >
Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University
NSTL
Elsevier
