Abstract
The intelligent monitoring and diagnosis of steel defects plays an important role in improving steel quality, production efficiency, and associated smart manufacturing. The application of the bio-inspired algorithms to mechanical engineering problems is of great significance. The split attention network is an improvement of the residual network, and it is an improvement of the visual attention mechanism in the bionic algorithm. In this paper, based on the feature pyramid network and split attention network, the network is improved and optimised in terms of data enhancement, multi-scale feature fusion and network structure optimisation. The DF-ResNeSt50 network model is proposed, which introduces a simple modularized split attention block, which can improve the attention mechanism of cross-feature graph groups. Finally, experimental validation proves that the proposed network model has good performance and application prospects in the intelligent detection of steel defects.
Highlights
The application of Bio-inspired computation and artificial intelligence technology is gradually taking an important position in the field of mechanical engineering
Based on the good ecology and scalability of the Python language and the open source framework PyTorch, this article uses a series of open source libraries and toolkits to implement the overall algorithm program (Neuhauser et al, 2020; Sun et al, 2020). Such as: Numpy, Albumentations, segmentation_models.pytorch semantic segmentation model library, etc. These open source tools greatly save the development time of the defect detection and segmentation program in this article, so that more time and energy can be invested in the research, improvement and experiment of the algorithm
In order to solve the problem of steel defects with different sizes, low contrast and different defect categories, this paper uses the DF-ResNeSt50 network model to investigate steel defects
Summary
The application of Bio-inspired computation and artificial intelligence technology is gradually taking an important position in the field of mechanical engineering. Bio-inspired algorithms can replace humans to a certain extent, through training and learning to complete the tedious task of detecting steel surface defects (Chen et al, 2021a; Yang et al, 2021; Yun et al, 2021, 2022). Research on steel plate defect detection based on visual attention mechanisms and bionic algorithms will help the steel industry move towards intelligence and information. 2) Based on the visual attention mechanism in the bio-inspired algorithms, combined with the feature pyramid network, on the basis of the residual network, a simple modular splitattention block is added, and the DF-ResNeSt50 network is proposed. An improved split-attention network based on the visual attention mechanism in bionic computing is proposed for residual networks and feature pyramid networks.
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