A lightweight multi scale fusion network for IGBT ultrasonic tomography image segmentation

The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor device, and the integrity of its internal structure directly influences both its electrical performance and long-term reliability. However, the precise semantic segmentation of IGBT ultrasonic tomographic images poses several challenges, primarily due to high-density noise interference and visual distortion caused by target warping. To address these challenges, this paper constructs a dedicated IGBT ultrasonic tomography (IUT) dataset using Scanning Acoustic Microscopy (SAM) and proposes a lightweight Multi-Scale Fusion Network (LMFNet) aimed at improving segmentation accuracy and processing efficiency in ultrasonic images analysis. LMFNet adopts a deep U-shaped encoder-decoder architecture, with the backbone designed using inverted residual blocks to optimize feature transmission while maintaining model compactness. Additionally, we introduce two flexible, plug-and-play modules: the Context Feature Fusion (CFF) module, which effectively integrates multi-scale contextual information at skip connection layers, and the Multi-Scale Perception Aggregation (MPA) module, which focuses on extracting and fusing multi-scale features at bottleneck layers. Experimental results demonstrate that LMFNet performs exceptionally well on the IUT dataset, significantly outperforming existing methods in terms of segmentation accuracy and model lightweighting performance.