A modified-YOLOv5s model for detection of wire braided hose defects. (28th February 2022)

• Record Type: Journal Article
• Authors: Ying, Zhiping; Lin, Zhongtao; Wu, Zhenyu; Liang, Kuan; Hu, XuDong
• Is Part Of: Measurement. Volume 190(2022)
• Publication Date: 2022-02-28
• Subjects: Wire braided hoses -- Deep learning -- Defect detection -- YOLOv5 -- Machine vision; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110683
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A modified-YOLOv5s was proposed to detect defects of wire braided hoses. K-means++ clustering algorithm is adopted to obtain more suitable anchor boxes. Focal loss function was adopted to balance between negative and positive samples. New tailored prediction heads and efficient channel attention (ECA) mechanism were used to improve the detection performance. Abstract: High-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.