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Research on real-time detection of large-granularity green pellets based on YOLOV3 algorithm

Z. Yang orcid.org/0000-0002-3228-9879 ; College of Applied Technology, University of Science and Technology Liaoning, Anshan, Liaoning, China *

* Dopisni autor.

Sažetak

In order to realize the real-time detection of abnormal green pellet particle size. First, image data of large-granularity green balls at different disk pelletizing machine material disk speeds and different camera angles are collected on site; then LabelImg software is used to label the image data of large-granularity green balls; and finally based on the YOLOv3 algorithm under the Pytorch deep learning framework train and detect large-grained ball image data. The experimental results show that: under the condition of high rotation speed of the material disk of the disc pelletizing machine, the detection accuracy can reach more than 90,58 % for the image data of a single large-grained green ball, and the comprehensive detection rate can reach more than 85 %.

Ključne riječi

green pellets; detection; granularity; deep learning; YOLOv3

Hrčak ID:

315665

URI

https://hrcak.srce.hr/315665

Datum izdavanja:

1.7.2024.

Posjeta: 837 *