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Research on salient object detection algorithm for complex electrical components
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AI
DOI：10.1007/s10845-024-02434-y.png)
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Abstract
En 中文
Due to the complexity of electrical components, traditional edge detection methods cannot always accurately extract key edge features of them. Therefore, this study constructs a dataset of complex electrical components and proposes a Step-by-Level Multi-Scale Extraction, Fusion, and Refinement Network (SMFRNet) that is based on the salient object detection algorithm. As detailed features includes a wealth of texture and shape characteristics that are related to edges, so the Hierarchical Deep Aggregation U-block (HDAU) is incorporated in the encoder as a means of capturing more details through hierarchical aggregation. Meanwhile, the proposed Multi-Scale Pyramid Convolutional Fusion (MPCF) and Fusion Attention Structure (FAS) achieve step-by-level feature refinement to obtain finer edges. In order to address the issues of imbalanced pixel categories and the difficulty in separating edge pixels, a hybrid loss function is also constructed. The experimental results indicate that this method outperforms nine state-of-the-art algorithms, enabling the extraction of high-precision key edge features. It provides a reliable method for key edge extraction in complex electrical components and provides important technical support for automated components measurement.
Keywords:
Complex electrical components
Key edge feature extraction
Salient object detection
Boundary refinement
Journal
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7.4
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3.5K
Citations:
1.1W