A Classification Model for Fine-Grained Silkworm Cocoon Images Based on Bilinear Pooling and Adaptive Feature Fusion
Mochen Liu,
Xin Hou,
Mingrui Shang,
Eunice Oluwabunmi Owoola,
Guizheng Zhang,
Wei Wei,
Zhanhua Song and
Yinfa Yan ()
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Mochen Liu: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Xin Hou: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Mingrui Shang: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Eunice Oluwabunmi Owoola: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Guizheng Zhang: Sericulture Technology Extension Station of Guangxi Zhuang Autonomous Region, Nanning 530000, China
Wei Wei: Sericulture Technology Extension Station of Guangxi Zhuang Autonomous Region, Nanning 530000, China
Zhanhua Song: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Yinfa Yan: College of Mechanical and Electrical Engineering, Shandong Agriculture University, Tai’an 271018, China
Agriculture, 2024, vol. 14, issue 12, 1-19
Abstract:
The quality of silkworm cocoons affects the quality and cost of silk processing. It is necessary to sort silkworm cocoons prior to silk production. Cocoon images consist of fine-grained images with large intra-class differences and small inter-class differences. The subtle intra-class features pose a serious challenge in accurately locating the effective areas and classifying silkworm cocoons. To improve the perception of intra-class features and the classification accuracy, this paper proposes a bilinear pooling classification model (B-Res41-ASE) based on adaptive multi-scale feature fusion and enhancement. B-Res41-ASE consists of three parts: a feature extraction module, a feature fusion module, and a feature enhancement module. Firstly, the backbone network, ResNet41, is constructed based on the bilinear pooling algorithm to extract complete cocoon features. Secondly, the adaptive spatial feature fusion module (ASFF) is introduced to fuse different semantic information to solve the problem of fine-grained information loss in the process of feature extraction. Finally, the squeeze and excitation module (SE) is used to suppress redundant information, enhance the weight of distinguishable regions, and reduce classification bias. Compared with the widely used classification network, the proposed model achieves the highest classification performance in the test set, with accuracy of 97.0% and an F 1- score of 97.5%. The accuracy of B-Res41-ASE is 3.1% and 2.6% higher than that of the classification networks AlexNet and GoogLeNet, respectively, while the F 1- score is 2.5% and 2.2% higher, respectively. Additionally, the accuracy of B-Res41-ASE is 1.9% and 7.7% higher than that of the Bilinear CNN and HBP, respectively, while the F 1- score is 1.6% and 5.7% higher. The experimental results show that the proposed classification model without complex labelling outperforms other cocoon classification algorithms in terms of classification accuracy and robustness, providing a theoretical basis for the intelligent sorting of silkworm cocoons.
Keywords: silkworm cocoon; bilinear pooling; fine-grained image classification; adaptive spatial feature fusion; feature enhancement (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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