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Real-time detection of wood defects based on SPP-improved YOLO algorithm

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Abstract

Wood processing is one of the most widely used in agriculture and industry. Low precision and high time delay of machine learning in wood defect detection are currently the main factors restricting the production efficiency and product quality of the wood processing industry. An SPP-improved deep learning method was proposed to detect wood defects based on the basic framework of the YOLO V3 network to improve accuracy and real-time performance. The extended dataset was firstly established by image data enhancement and preprocessing based on the limited samples of the wood defect dataset. Anchor box scale re-clustering of the wood defect dataset was carried out according to the defect features. The spatial pyramid pooling (SPP) network was applied to improve the feature pyramid (FP) network in YOLO V3. The validity and real-time performance of the proposed algorithm were verified by a randomly selected test set. The results show that the overall detection accuracy rate on the wood defect test dataset reaches 93.23% while the detection time for each image is within 13 ms.

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Funding

This work was supported in part by the National Key R&D Program of China under Grant 2018YFB1308303, and in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221116, China

    Yuming Cui

  2. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Shuochen Lu & Songyong Liu

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  1. Yuming Cui
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  2. Shuochen Lu
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  3. Songyong Liu
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Contributions

Yuming Cui: Validation, Formal analysis, Investigation, Writing - Original Draft, Visualization. Shuochen Lu: Methodology, Software, Resources, Data curation. Songyong Liu: Conceptualization, Writing - Review & Editing, Supervision, Project administration.

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Correspondence to Shuochen Lu.

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Cui, Y., Lu, S. & Liu, S. Real-time detection of wood defects based on SPP-improved YOLO algorithm. Multimed Tools Appl 82, 21031–21044 (2023). https://doi.org/10.1007/s11042-023-14588-7

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  • DOI: https://doi.org/10.1007/s11042-023-14588-7

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