WormNet: A Multi-View Network for Silkworm Re-Identification
Re-identification (ReID) has been widely applied in person and vehicle recognition tasks. This study extends its application to a novel domain: insect (silkworm) recognition. However, unlike person or vehicle ReID, silkworm ReID presents unique challenges, such as the high similarity between individ...
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2025-07-01
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| author | Hongkang Shi Minghui Zhu Linbo Li Yong Ma Jianmei Wu Jianfei Zhang Junfeng Gao |
| author_facet | Hongkang Shi Minghui Zhu Linbo Li Yong Ma Jianmei Wu Jianfei Zhang Junfeng Gao |
| author_sort | Hongkang Shi |
| collection | DOAJ |
| description | Re-identification (ReID) has been widely applied in person and vehicle recognition tasks. This study extends its application to a novel domain: insect (silkworm) recognition. However, unlike person or vehicle ReID, silkworm ReID presents unique challenges, such as the high similarity between individuals, arbitrary poses, and significant background noise. To address these challenges, we propose a multi-view network for silkworm ReID, called WormNet, which is built upon an innovative strategy termed extraction purification extraction interaction. Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. Next, a feature mask module (FMM) is employed to purify the features in the spatial domain, thereby reducing the impact of background interference. To further enhance the data representation capabilities of the network, we propose a channel interaction module (CIM), which combines an efficient channel attention network with global response normalization (GRN) in parallel to recalibrate features, enabling the network to learn crucial information at both the local and global scales. Additionally, we introduce a new silkworm ReID dataset for network training and evaluation. The experimental results demonstrate that WormNet achieves an mAP value of 54.8% and a rank-1 value of 91.4% on the dataset, surpassing both state-of-the-art and related networks. This study offers a valuable reference for ReID in insects and other organisms. |
| format | Article |
| id | doaj-art-dfa64faf0aef471e99ce312af40e8b75 |
| institution | DOAJ |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-dfa64faf0aef471e99ce312af40e8b752025-08-20T02:48:19ZengMDPI AGAnimals2076-26152025-07-011514201110.3390/ani15142011WormNet: A Multi-View Network for Silkworm Re-IdentificationHongkang Shi0Minghui Zhu1Linbo Li2Yong Ma3Jianmei Wu4Jianfei Zhang5Junfeng Gao6Sericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong 637000, ChinaSericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong 637000, ChinaSericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong 637000, ChinaSericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong 637000, ChinaSericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong 637000, ChinaSericultural Research Institute, Sichuan Academy of Agricultural Sciences, Nanchong 637000, ChinaDepartment of Computer Science, University of Aberdeen, Aberdeen AB24 3FX, UKRe-identification (ReID) has been widely applied in person and vehicle recognition tasks. This study extends its application to a novel domain: insect (silkworm) recognition. However, unlike person or vehicle ReID, silkworm ReID presents unique challenges, such as the high similarity between individuals, arbitrary poses, and significant background noise. To address these challenges, we propose a multi-view network for silkworm ReID, called WormNet, which is built upon an innovative strategy termed extraction purification extraction interaction. Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. Next, a feature mask module (FMM) is employed to purify the features in the spatial domain, thereby reducing the impact of background interference. To further enhance the data representation capabilities of the network, we propose a channel interaction module (CIM), which combines an efficient channel attention network with global response normalization (GRN) in parallel to recalibrate features, enabling the network to learn crucial information at both the local and global scales. Additionally, we introduce a new silkworm ReID dataset for network training and evaluation. The experimental results demonstrate that WormNet achieves an mAP value of 54.8% and a rank-1 value of 91.4% on the dataset, surpassing both state-of-the-art and related networks. This study offers a valuable reference for ReID in insects and other organisms.https://www.mdpi.com/2076-2615/15/14/2011insect re-identificationmulti-order extractionchannel interactionspatial purificationsilkworm |
| spellingShingle | Hongkang Shi Minghui Zhu Linbo Li Yong Ma Jianmei Wu Jianfei Zhang Junfeng Gao WormNet: A Multi-View Network for Silkworm Re-Identification Animals insect re-identification multi-order extraction channel interaction spatial purification silkworm |
| title | WormNet: A Multi-View Network for Silkworm Re-Identification |
| title_full | WormNet: A Multi-View Network for Silkworm Re-Identification |
| title_fullStr | WormNet: A Multi-View Network for Silkworm Re-Identification |
| title_full_unstemmed | WormNet: A Multi-View Network for Silkworm Re-Identification |
| title_short | WormNet: A Multi-View Network for Silkworm Re-Identification |
| title_sort | wormnet a multi view network for silkworm re identification |
| topic | insect re-identification multi-order extraction channel interaction spatial purification silkworm |
| url | https://www.mdpi.com/2076-2615/15/14/2011 |
| work_keys_str_mv | AT hongkangshi wormnetamultiviewnetworkforsilkwormreidentification AT minghuizhu wormnetamultiviewnetworkforsilkwormreidentification AT linboli wormnetamultiviewnetworkforsilkwormreidentification AT yongma wormnetamultiviewnetworkforsilkwormreidentification AT jianmeiwu wormnetamultiviewnetworkforsilkwormreidentification AT jianfeizhang wormnetamultiviewnetworkforsilkwormreidentification AT junfenggao wormnetamultiviewnetworkforsilkwormreidentification |