Research on salient object detection algorithm for complex electrical components

Abstract

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.