From Segmentation to Classification: A Deep Learning Scheme for Sintered Surface Images Processing
Yi Yang,
Tengtuo Chen,
Liang Zhao
Abstract:Effectively managing the quality of iron ore is critical to iron and steel metallurgy. Although quality inspection is crucial, the perspective of sintered surface identification remains largely unexplored. To bridge this gap, we propose a deep learning scheme for mining the necessary information in sintered images processing to replace manual labor and realize intelligent inspection, consisting of segmentation and classification. Specifically, we first employ a DeepLabv3+ semantic segmentation algorithm to ext… Show more
“…Deep learning is regarded as an excellent tool for simulating the structure and thinking of the human brain. To effectively manage the quality of iron ore, a deep learning scheme for mining the necessary information in sintered image processing [40] was proposed to replace manual labor and realize intelligent inspection. Experiments showed that the improved semantic segmentation model can effectively segment the sintered surface, achieving 98.01% segmentation accuracy with only a 5.71 MB size.…”
Section: Data-driven Optimization and Deep Leaning Methodsmentioning
“…Deep learning is regarded as an excellent tool for simulating the structure and thinking of the human brain. To effectively manage the quality of iron ore, a deep learning scheme for mining the necessary information in sintered image processing [40] was proposed to replace manual labor and realize intelligent inspection. Experiments showed that the improved semantic segmentation model can effectively segment the sintered surface, achieving 98.01% segmentation accuracy with only a 5.71 MB size.…”
Section: Data-driven Optimization and Deep Leaning Methodsmentioning
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