WaveSegNet: An Efficient Method for Scrap Steel Segmentation Utilizing Wavelet Transform and Multiscale Focusing

Jiakui Zhong, Yunfeng Xu () and Changda Liu
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Jiakui Zhong: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Yunfeng Xu: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Changda Liu: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

Mathematics, 2024, vol. 12, issue 9, 1-21

Abstract: Scrap steel represents a sustainable and recyclable resource, instrumental in diminishing carbon footprints and facilitating the eco-friendly evolution of the steel sector. However, current scrap steel recycling faces a series of challenges, such as high labor intensity and occupational risks for inspectors, complex and diverse sources of scrap steel, varying types of materials, and difficulties in quantifying and standardizing manual visual inspection and rating. Specifically, we propose WaveSegNet, which is based on wavelet transform and a multiscale focusing structure for scrap steel segmentation. Firstly, we utilize wavelet transform to process images and extract features at different frequencies to capture details and structural information in the images. Secondly, we introduce a mechanism of multiscale focusing to further enhance the accuracy of segmentation by extracting and perceiving features at different scales. Through experiments conducted on the public Cityscapes dataset and scrap steel datasets, we have found that WaveSegNet consistently demonstrates superior performance, achieving the highest scores on the mIoU metric. Particularly notable is its performance on the real-world scrap steel dataset, where it outperforms other segmentation algorithms with an average increase of 3.98% in mIoU(SS), reaching 69.8%, and a significant boost of nearly 5.98% in mIoU(MS), achieving 74.8%. These results underscore WaveSegNet’s exceptional capabilities in processing scrap steel images. Additionally, on the publicly available Cityscapes dataset, WaveSegNet shows notable performance enhancements compared with the next best model, Segformer. Moreover, with its modest parameters and computational demands (34.1 M and 322 GFLOPs), WaveSegNet proves to be an ideal choice for resource-constrained environments, demonstrating high computational efficiency and broad applicability. These experimental results attest to the immense potential of WaveSegNet in intelligent scrap steel rating and provide a new solution for the scrap steel recycling industry. These experimental results attest to the immense potential of WaveSegNet in intelligent scrap steel rating and provide a new solution for the scrap steel recycling industry.

Keywords: intelligent detection; semantic segmentation; wavelet transform; multiscale focusing; scrap steel (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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