Name
Abstract | ||
|---|---|---|
Scrap steel grading is relatively important during the process of scrap recycling. However, the current evaluation method for scrap steel grading mainly relies on the manual, which is subjective and inaccurate. There are two challenges for scrap steel grading, including detecting scrap steel precisely and evaluating its grade accurately. To address these issues, we propose a novel framework, including a carriage-attention module (CaM), a scrap detection module (SDM), and a scrap grading module (SGM). Owing to its complex background and large size range, it is a challenging task to detect scrap steel. To this end, the CaM is proposed to remove the complex background from the original scrap steel images. The SDM is then proposed to detect scrap steel in the carriage-only images that the complex background is removed. Specifically, the SDM is a segmentation network with a multi-scale feature fused pyramid (MFFP). Finally, based on a weight-balanced Bayes classifier, the SGM is developed to evaluate the final grades for all images captured from a carriage during the process of scrap unloading. Experimental results show that our approach obtains a high accuracy for scrap steel grading while ensuring a fast inference speed. Note that our method has been successfully applied in several steel smelters. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIM.2022.3206816 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Steel, Image segmentation, Task analysis, Semantics, Recycling, Feature extraction, Interference, Bayes formula, convolutional neural network (CNN), deep learning (DL), scrap steel grading | Journal | 71 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 5 | |