CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects

Yihai Ma; Guowu Yuan; Kun Yue; Hao Zhou; Yihai Ma; Guowu Yuan; Kun Yue; Hao Zhou

doi:10.3934/mbe.2023795

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 10: 17886-17904. doi: 10.3934/mbe.2023795

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Research article Special Issues

CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects

1.
School of Information Science and Engineering, Yunnan University, Kunming 650504, China
2.
Yunnan Key Laboratory of Intelligent Systems and Computing, Kunming 650504, China

Academic Editor: Shangce Gao

Received: 27 July 2023 Revised: 26 August 2023 Accepted: 11 September 2023 Published: 18 September 2023

In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU (SIoU)). This model incorporates the C2F module proposed in the state-of-the-art object detection network YOLOv8 (You Only Look Once Version 8). This module optimizes the network by parallelizing additional gradient flow branches, enhancing the model's feature extraction capability and obtaining richer gradient information. Furthermore, this model uses Jump Concat to preserve minor defect feature information during the fusion process in the feature fusion pyramid's P4 layer. Additionally, this model integrates the SIoU localization loss function to improve localization accuracy and detection precision. Experimental results demonstrate that our proposed CJS-YOLOv5n model achieves superior overall performance. It maintains a detection speed of over 500 FPS (frames per second) while increasing the recall rate by 2.3% and mAP (mean average precision)@0.5 by 1.7%. The proposed model is suitable for application in high-speed cigarette production lines.
- cigarette,
- defect detection,
- YOLOv5n,
- C2F,
- Jump Concat,
- SIoU
Citation: Yihai Ma, Guowu Yuan, Kun Yue, Hao Zhou. CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 17886-17904. doi: 10.3934/mbe.2023795

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Abstract

In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU (SIoU)). This model incorporates the C2F module proposed in the state-of-the-art object detection network YOLOv8 (You Only Look Once Version 8). This module optimizes the network by parallelizing additional gradient flow branches, enhancing the model's feature extraction capability and obtaining richer gradient information. Furthermore, this model uses Jump Concat to preserve minor defect feature information during the fusion process in the feature fusion pyramid's P4 layer. Additionally, this model integrates the SIoU localization loss function to improve localization accuracy and detection precision. Experimental results demonstrate that our proposed CJS-YOLOv5n model achieves superior overall performance. It maintains a detection speed of over 500 FPS (frames per second) while increasing the recall rate by 2.3% and mAP (mean average precision)@0.5 by 1.7%. The proposed model is suitable for application in high-speed cigarette production lines.

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