A Lightweight Surface Defect Segmentation Network with External Semantics and High-frequency Information

Surface defect detection is an extremely challenging task. Surface defects typically exhibit weak appearances and complex boundaries. Some detection methods are limited by their high computational and storage costs when applied to devices with limited resources. Although some lightweight methods can achieve real-time inference speeds, their accuracy in detecting defects in complex scenarios remains insufficient. To address this challenge, we have designed a lightweight defect segmentation network with external semantics and high-frequency information. Firstly, we devised an External Semantic Attention (ESA) mechanism with linear complexity. This attention learns potential semantic relationships between samples by computing the correlation between input samples and external storage units in both spatial and channel dimensions, thus enhancing the network's attention to defects of weak appearances. Secondly, we designed a High-Frequency Detail Perception Block (HDPB) that enables interaction between image features and high-frequency information, allowing the network to actively learn defect boundary features and capture more details of surface defects. Extensive experiments on three publicly available datasets have shown that our method outperforms existing lightweight segmentation methods.