Metalurgija, Vol. 63 No. 3-4, 2024.
Izvorni znanstveni članak
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
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
Datum izdavanja:
1.7.2024.
Posjeta: 92 *