基于改进Mask R-CNN模型的粘连烟丝识别方法

张伟伟; 姬远鹏; 元春波; 王君婷; 齐晓任; 张卫正; 李萌; 饶智

doi:10.12187/2024.05.009

October 2024

Article Contents

ZHANG Weiwei, JI Yuanpeng, YUAN Chunbo, et al. Adhesive tobacco shreds recognition method based on improved Mask R-CNN model[J]. Journal of Light Industry, 2024, 39(5): 78-85. doi: 10.12187/2024.05.009

Citation: ZHANG Weiwei, JI Yuanpeng, YUAN Chunbo, et al. Adhesive tobacco shreds recognition method based on improved Mask R-CNN model[J]. Journal of Light Industry, 2024, 39(5): 78-85. doi: 10.12187/2024.05.009 shu

Adhesive tobacco shreds recognition method based on improved Mask R-CNN model

ZHANG Weiwei ¹ ,
JI Yuanpeng ¹ ,
YUAN Chunbo ² ,
WANG Junting ³ ,
QI Xiaoren ⁴ ,
ZHANG Weizheng ¹ ,
LI Meng ⁵ ,
RAO Zhi ^6,,

1. College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
2. Ningbo Tobacco Factory, China Tobacco Zhejiang Industrial Co., Ltd., Ningbo 315504, China;
3. Technical Center, China Tobacco Hubei Industrial Co., Ltd., Wuhan 430040, China;
4. Anyang Company of Henan Tobacco Company, Anyang 455002, China;
5. College of Tobacco Science and Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
6. Hongyun Honghe Tobacco(Group)Co., Ltd., Kunming 650032, China

Corresponding author: RAO Zhi, 403329531@qq.com
Received Date: 2023-11-09
Accepted Date: 2023-12-29
Available Online: 2024-10-15

Abstract

To achieve accurate identification and efficient segmentation of adhesive tobacco shreds, a method for adhesive tobacco shreds recognition based on an improved Mask R-CNN (Mask Region-based Convolutional Neural Network) model was proposed. Firstly, adhesive tobacco shreds images were collected, and the dataset was augmented through image enhancement operations to expand it to the required sample size for training the model. Secondly, edge feature extraction and fractal feature extraction were performed on the adhesive tobacco shreds images in the training set based on Mask R-CNN, resulting in clearer and more continuous image edge features and texture feature information. Subsequently, the original features, edge features, and fractal features were fused to fully utilize features at different levels and enrich low-level features. Finally, by introducing a hybrid attention mechanism that focused on both channel and spatial dimensions of feature maps, the efficiency and accuracy of tobacco shred recognition were improved. Experimental results showed that the mean intersectionover union (Avg.MIoU) of the recognition method based on the improved Mask R-CNN model was 85.29%, and the mean class pixel accuracy (Avg.MPA) was 84.33%, under different adhesion conditions enabling precise identification of tobacco shreds and outperforming the original Mask R-CNN and DeepLabV3+ models. This method could rapidly and accurately identify and segment adhesive tobacco shreds, providing technical support for subsequent tobacco shred width detection.
- adhesive tobacco shred,
- improved Mask R-CNN model,
- edge feature extraction,
- feature fusion,
- hybrid attention mechanism
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沈阳化工大学材料科学与工程学院沈阳 110142

Adhesive tobacco shreds recognition method based on improved Mask R-CNN model

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