Detection of cigar appearance defects based on improved YOLOv5

Suxiao Li; Hongbin Guo; Qing Zhang; Qian Miao; Xiaofang Zhang; Pengfei Zhang

doi:10.1117/12.3009569

16 October 2023 Detection of cigar appearance defects based on improved YOLOv5

Suxiao Li, Hongbin Guo, Qing Zhang, Qian Miao, Xiaofang Zhang, Pengfei Zhang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030Q (2023) https://doi.org/10.1117/12.3009569
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The detection of cigar appearance defects is a critical step in the cigar production process and holds significant importance in ensuring the quality of cigar production. To address the issues of inefficient and unstable manual detection, this paper employed deep learning-based object detection models for cigar appearance defect detection and proposed an algorithm for cigar appearance detection based on YOLOv5. First, the cigar appearance defect acquisition equipment was constructed, the defect image data were collected, and the experimental dataset was established. Then, the deformable convolution was introduced to enhance the learning capability of the backbone network. Furthermore, the Bi-directional Feature Pyramid Network (BiFPN) was employed to improve the feature information of each layer. Lastly, the spatial context pyramid (SCP) was utilized to enable global spatial context learning within the feature layers, further enhancing the features. The model performance was evaluated by mean average precision (mAP). Experimental results demonstrated that the improved YOLOv5 achieved a mAP of 90.7% for cigar appearance defect detection and a detection speed of 10.6ms per image, showcasing excellent detection accuracy and speed. Moreover, this model exhibited significant improvement in detecting small defects and defects located at the edges. Therefore, the improved YOLOv5 model satisfied the requirements for automatic cigar appearance defect detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Suxiao Li, Hongbin Guo, Qing Zhang, Qian Miao, Xiaofang Zhang, and Pengfei Zhang "Detection of cigar appearance defects based on improved YOLOv5", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030Q (16 October 2023); https://doi.org/10.1117/12.3009569

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KEYWORDS

Defect detection

Machine learning

Convolution

Deformation

Inspection

Education and training

Object detection

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