Research on improved YOLOv5-based defect detection algorithm for cigarette air thinning head

Wang Shuhai; Li Ruihong; Han Ming; Xu Zhijun

doi:10.1117/12.3005808

10 October 2023 Research on improved YOLOv5-based defect detection algorithm for cigarette air thinning head

Wang Shuhai, Li Ruihong, Han Ming, Xu Zhijun

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127990W (2023) https://doi.org/10.1117/12.3005808
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Cigarette empty thin head defect detection is an important step to ensure product quality in tobacco factories. To address the current problems about detecting thin head with little content and low accuracy, a cigarette empty thin head defect detection algorithm based on improved YOLOv5s is proposed. Firstly, a convolutional block attention mechanism is introduced between neck and head to emphasize the extraction of empty thin head defect features; then a weighted bidirectional feature pyramid structure is used to improve the neck network and enhance the feature fusion capability of the model; finally, a lightweight module is designed to reduce the computational complexity of the model. The experimental results show that the improved algorithm can effectively detect cigarette hollow-head defects with an improved accuracy of 2.1%, and the model parameters are only 5.35M, which can provide technical support for subsequent cigarette hollow-head defect detection.

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

Citation Download Citation

Wang Shuhai, Li Ruihong, Han Ming, and Xu Zhijun "Research on improved YOLOv5-based defect detection algorithm for cigarette air thinning head", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127990W (10 October 2023); https://doi.org/10.1117/12.3005808

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KEYWORDS

Head

Defect detection

Detection and tracking algorithms

Neck

Feature fusion

Object detection

Target detection

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