An Improved Model Based on YOLO v5s for Intelligent Detection of Center Porosity in Round Bloom

DOI Web Site 参考文献12件
  • Xiao Zi-xuan
    School of Metallurgical Engineering, Anhui University of Technology
  • Zhu Zheng-hai
    School of Metallurgical Engineering, Anhui University of Technology
  • Wei Guang-xu
    School of Metallurgical Engineering, Anhui University of Technology
  • Liang Shang-Dong
    School of Metallurgical Engineering, Anhui University of Technology
  • Yang Cheng-cheng
    School of Metallurgical Engineering, Anhui University of Technology
  • Zheng Xiang
    School of Metallurgical Engineering, Anhui University of Technology
  • Huang Dong-jian
    School of Metallurgical Engineering, Anhui University of Technology
  • He Fei
    School of Metallurgical Engineering, Anhui University of Technology

抄録

<p>To address the problem that speed and accuracy cannot be taken into account in intelligent detection models of center porosity in round bloom, an improved model for it based on YOLO v5s (the fifth version of You only look once) was determined by establishing a data set of about 10000 images, setting up a contrast experiment and an ablation experiment embedded with Coordinate Attention and Slim-neck modules. The results show that the improved YOLO v5s has good detection performance: mAP@0.5 of the verification set reaches 99.17%, which is respectively 0.2%, 0.1%, 2.9% and 1.7% higher than Faster RCNN, SSD, YOLO v3-Tiny and YOLO v5s; the detection speed is 86 fps, which is respectively 514.2%, 168.8% higher than Faster RCNN, SSD and maintains the speed of the original YOLO v5s while its accuracy is improved. The operation time of a single picture in the testing set is only 0.015 s, which could be implemented the achieve real-time and accurate location of center porosity in round bloom. This study provides a new method for the research of detecting center porosity, which is helpful to the development of intelligent detection of defects in continuous casting billet.</p>

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