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Design and fabrication of a high-efficiency defect inspection prototype for diary plastic cutlery based on machine-vision with improved deep leaning algorithm

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Abstract

Plastic cutlery is widely used in dairy industry, with a huge production volume. Defects inevitably arise from factors such as raw material qualities, production processes, and environmental conditions. Traditionally, these defects are inspected manually, leading to high costs, high intensity, low accuracy, and reduced productivity. To address these issues, a high-efficiency defect detection prototype was designed and manufactured to accomplish the automatic sorting task of plastic cutlery. An improved deep learning model, YOLO-spoon, is proposed and integrated into the prototype. To tackle the characteristics of small product defects and packaging glare, Squeeze-and-Excitation attention module is employed to enhance the network's focus on defect features, thereby improving the accuracy of target defect detection. The C3 module of the backbone network is replaced with the attention module to reduce the network's complexity and enhance real-time performance. Experimental results demonstrate that, compared to the original algorithm, the average precision (mAP@0.5) of the proposed algorithm increased by 2.8%, with only 7.3ms detection time. The proposed algorithm also outperforms all the other mainstream algorithms by over 5.6%. Compared to the manual inspection, by using the proposed prototype, the three-year total cost reduced by 58.9%, the detection accuracy increased by 15.5%, and annual production capacity increased by 92.3%. The proposed prototype fulfills the requirement of high-efficiency automatic defect inspection for diary plastic cutlery.

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Funding

Jiangsu Key R&D Program BE2020082-1.

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Authors and Affiliations

  1. College of Mechanical Engineering, Yangzhou University, Yangzhou, 225127, China

    Jian Yang, Yu Qin, Zhida Zhu & Dong Guan

  2. College of Mechanical and Electrical Engineering, Hohai University, Changzhou, 213022, China

    Xiaobin Xu

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  1. Jian Yang
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  2. Yu Qin
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  3. Zhida Zhu
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  4. Xiaobin Xu
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  5. Dong Guan
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Contributions

Jian Yang: conceptualization, writing. Yu Qin: methodology, investigation, software, writing. Zhida Zhu: investigation. Xiaobin Xu: validation. Dong Guan: reviewing and editing.

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Correspondence to Jian Yang or Dong Guan.

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Yang, J., Qin, Y., Zhu, Z. et al. Design and fabrication of a high-efficiency defect inspection prototype for diary plastic cutlery based on machine-vision with improved deep leaning algorithm. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19395-2

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