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PCP-YOLO: an approach integrating non-deep feature enhancement module and polarized self-attention for small object detection of multiscale defects

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Abstract

The detection of small objects within multiscale defects amidst complex background interference presents a formidable challenge in industrial defect detection. To address this issue and achieve precise and expeditious identification in industrial defect detection, this study proposes PCP-YOLO, a novel network that incorporates a non-deep feature extraction module and a polarized filtering feature fusion module for small object defect detection. Initially, YOLOv8 is employed as the foundational model. Subsequently, a lightweight, non-deep feature extraction module, PotentNet, is designed and integrated into the backbone network. In the neck network, a feature fusion module incorporating polarized self-attention, C2f_ParallelPolarized, has been developed. Finally, CARAFE is utilized to substitute the original upsampling module in the neck network. The efficacy of this approach has been rigorously evaluated using three datasets: the publicly available NEU-DET and PKU-PCB datasets, and the real-world industrial dataset GC10-DET. The mAP@0.5 values achieved are 79.4%, 96.1%, and 77.6%, significantly outperforming other detection methods. The method also has a fast inference speed. These results demonstrate that PCP-YOLO exhibits substantial potential for rapid and accurate defect detection.

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No datasets were generated or analysed during the current study.

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Acknowledgements

This study was supported by the Director’s Fund of the Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University (Grant No. IBES2024ZR01), the Mass Spectrometry Key Technology R&D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments (GRANT: 2023ZPLH07), and the Anhui Province Graduate Education Quality Engineering Project (GRANT: 2023cxcysj129).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei, 230601, Anhui, China

    Penglin Wang & Donghui Shi

  2. Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, Hefei, 230022, Anhui, China

    Donghui Shi

  3. Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia

    Jose Aguilar

  4. Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Merida, Venezuela

    Jose Aguilar

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  1. Penglin Wang
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Contributions

PW (First Author): made substantial contributions to the conception of the work; the acquisition, analysis and interpretation of data; drafted the work. DS (Corresponding author): revised it critically for important intellectual content. JA: agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy.

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Correspondence to Donghui Shi.

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Wang, P., Shi, D. & Aguilar, J. PCP-YOLO: an approach integrating non-deep feature enhancement module and polarized self-attention for small object detection of multiscale defects. SIViP 19, 71 (2025). https://doi.org/10.1007/s11760-024-03666-4

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