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Pest-PVT: A model for multi-class and dense pest detection and counting in field-scale environments.

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Title: Pest-PVT: A model for multi-class and dense pest detection and counting in field-scale environments.
Authors: Chen, Hongrui1,2 (AUTHOR), Wen, Changji1,2 (AUTHOR) changjiw@jlau.edu.cn, Zhang, Long1,2 (AUTHOR), Ma, Zhenyu1,2 (AUTHOR), Liu, Tianyu1,2 (AUTHOR), Wang, Guangyao1,2 (AUTHOR), Yu, Helong1,2 (AUTHOR), Yang, Ce2,3 (AUTHOR), Yuan, Xiaohui2,4 (AUTHOR), Ren, Junfeng1,2 (AUTHOR) renjunfeng@mails.jlau.edu.cn
Source: Computers & Electronics in Agriculture. Mar2025, Vol. 230, pN.PAG-N.PAG. 1p.
Subjects: Object recognition (Computer vision), Transformer models, Feature extraction, Agriculture, Pests
Abstract: • The Pest-PVT framework addresses challenges in the Pest24 dataset, including small pest targets, high similarity, and dense distribution. • Pest-PVT uses an anchor-free FCOS method to improve small object detection and integrates ATSS to reduce sample imbalance bias. • Dynamic Heads tackle pests of different scales and spatial changes, while Shunted Self-Attention boosts multi-scale feature extraction and lowers memory use. Field-scale pest monitoring is crucial for evaluating insect infestations in agricultural environments. The Pest24 dataset presents unique challenges due to the small pest target sizes, high target similarity, and dense pest distribution. To address these challenges, we propose Pest-PVT, a comprehensive framework based on the Pyramid Vision Transformer v2 (PVTv2) network model. Pest-PVT adopts an anchor-free approach using Fully Convolutional One-Stage Object Detection (FCOS) to enhance small object detection and incorporates Adaptive Training Sample Selection (ATSS) to mitigate sample imbalance bias. Dynamic Heads (DyHead) are employed to handle pests of varying scales and spatial changes, while Shunted Self-Attention (SSA) enhances multi-scale feature capture and reduces memory consumption. Compared to 23 other mainstream detection models and previously published works, our proposed Pest-PVT outperforms state-of-the-art detectors on pest detection datasets, achieving the outperformed scores in detection evaluation metrics such as mAP, Precision, Recall, and F1-Score, reaching 77.2 %, 78.42 %, 81.27 %, and 0.80, respectively. With a parameter size of only 24.74 M, Pest-PVT is suitable for integration into edge devices with limited resources. This work represents a significant contribution to the field of field-scale pest monitoring and addresses the specific challenges posed by the Pest24 dataset. Our code is made available at https://github.com/jlauwcj/pest-pvt. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 183392841
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  Data: Pest-PVT: A model for multi-class and dense pest detection and counting in field-scale environments.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Hongrui%22">Chen, Hongrui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wen%2C+Changji%22">Wen, Changji</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> changjiw@jlau.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Long%22">Zhang, Long</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhenyu%22">Ma, Zhenyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Tianyu%22">Liu, Tianyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Guangyao%22">Wang, Guangyao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Helong%22">Yu, Helong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Ce%22">Yang, Ce</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Xiaohui%22">Yuan, Xiaohui</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Junfeng%22">Ren, Junfeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> renjunfeng@mails.jlau.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Electronics+in+Agriculture%22">Computers & Electronics in Agriculture</searchLink>. Mar2025, Vol. 230, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Agriculture%22">Agriculture</searchLink><br /><searchLink fieldCode="DE" term="%22Pests%22">Pests</searchLink>
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  Data: • The Pest-PVT framework addresses challenges in the Pest24 dataset, including small pest targets, high similarity, and dense distribution. • Pest-PVT uses an anchor-free FCOS method to improve small object detection and integrates ATSS to reduce sample imbalance bias. • Dynamic Heads tackle pests of different scales and spatial changes, while Shunted Self-Attention boosts multi-scale feature extraction and lowers memory use. Field-scale pest monitoring is crucial for evaluating insect infestations in agricultural environments. The Pest24 dataset presents unique challenges due to the small pest target sizes, high target similarity, and dense pest distribution. To address these challenges, we propose Pest-PVT, a comprehensive framework based on the Pyramid Vision Transformer v2 (PVTv2) network model. Pest-PVT adopts an anchor-free approach using Fully Convolutional One-Stage Object Detection (FCOS) to enhance small object detection and incorporates Adaptive Training Sample Selection (ATSS) to mitigate sample imbalance bias. Dynamic Heads (DyHead) are employed to handle pests of varying scales and spatial changes, while Shunted Self-Attention (SSA) enhances multi-scale feature capture and reduces memory consumption. Compared to 23 other mainstream detection models and previously published works, our proposed Pest-PVT outperforms state-of-the-art detectors on pest detection datasets, achieving the outperformed scores in detection evaluation metrics such as mAP, Precision, Recall, and F1-Score, reaching 77.2 %, 78.42 %, 81.27 %, and 0.80, respectively. With a parameter size of only 24.74 M, Pest-PVT is suitable for integration into edge devices with limited resources. This work represents a significant contribution to the field of field-scale pest monitoring and addresses the specific challenges posed by the Pest24 dataset. Our code is made available at https://github.com/jlauwcj/pest-pvt. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.compag.2024.109864
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Agriculture
        Type: general
      – SubjectFull: Pests
        Type: general
    Titles:
      – TitleFull: Pest-PVT: A model for multi-class and dense pest detection and counting in field-scale environments.
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            – D: 01
              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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