YOLOv5-S-G-B: a lightweight intelligent detection model for cardboard surface defects

In the manufacturing process of cardboard boxes, rapid and accurate surface defect detection is crucial for ensuring quality and preventing resource waste. However, current target detection models have large parameters that impact real-time processing and hinder deployment on devices with limited co...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2024-09, Vol.18 (10), p.6997-7011
Main Authors: Yang, Meng, Li, Dajun, Luo, Penghui, Wan, Xiaolong, Zhang, Zhixiang, Zhou, Lingning, Jia, Yong
Format: Article
Language:English
Subjects:
Accuracy
Boxes
Cardboard
Computer Imaging
Computer Science
Frames per second
Image Processing and Computer Vision
Lightweight
Multimedia Information Systems
Object recognition
Original Paper
Parameters
Pattern Recognition and Graphics
Real time
Signal,Image and Speech Processing
Surface defects
Target detection
Vision
Weight reduction
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Summary:In the manufacturing process of cardboard boxes, rapid and accurate surface defect detection is crucial for ensuring quality and preventing resource waste. However, current target detection models have large parameters that impact real-time processing and hinder deployment on devices with limited computational power, reducing both efficiency and applicability. To solve this challenge and better align with real-world application demands, a lightweight improved YOLOv5s model for detecting surface defects on cardboard boxes has been proposed in this study. Inspired by the lightweight features of ShuffleNetV2 and the Ghost model, and with the aim of achieving model lightweighting, the backbone is initially restructured through the incorporation of the ShuffleNetV2 primitive unit. Subsequently, we replace C3 and CBS with C3_Ghost and G_CBS, developing a lightweight backbone and neck structure. Ultimately, inter-layer skip connections are employed to design a feature fusion network applied in the neck to compensate for accuracy. The expriment results demonstrates that the improved YOLOv5-S-G-B model achieves significant reductions in parameters, GFLOPs, and weight dimensions-74.3%, 78.5%, and 72.3%, respectively, compared to the original YOLOv5s. It maintains an average accuracy of 94% and the average detection rate on less powerful CPUs increases from 7.00 frames per second (FPS). The YOLOv5-S-G-B model maintains high accuracy while featuring a significantly lightweight structure, making it better suited for real-world cardboard box defect detection applications.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-024-03369-w