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Flexible and robust detection for assembly automation with YOLOv5: a case study on HMLV manufacturing line

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  • Alexej Simeth

    (University of Luxembourg)

  • Atal Anil Kumar

    (University of Luxembourg)

  • Peter Plapper

    (University of Luxembourg)

Abstract

Automating assembly processes in High-Mix, Low Volume (HMLV) manufacturing remains challenging, especially for Small and Medium-sized Enterprises (SMEs). Consequently, many companies still rely on a significant amount of manual operations with an overall low degree of automation. The emergence of artificial intelligence-based algorithms offers potential solutions, enabling assembly automation compatible with multiple products and maintaining overall production flexibility. This paper investigates the application of the YOLO (You Only Look Once) object detection algorithm in an HMLV production line within an SME. The performance of the algorithm was tested for different cases, namely, (a) on different products having similar product features, (b) on completely new products, and (c) under different lighting conditions. The algorithm achieved precision and recall greater than 98 $$\%$$ % and mAP50:95 greater than 97 $$\%$$ % .

Suggested Citation

  • Alexej Simeth & Atal Anil Kumar & Peter Plapper, 2025. "Flexible and robust detection for assembly automation with YOLOv5: a case study on HMLV manufacturing line," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3447-3463, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02411-5
    DOI: 10.1007/s10845-024-02411-5
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