LACTNet: A Label-Aware CNN-Transformer Network for Surface Defect Segmentation

Accurate and efficient surface defect segmentation is essential in assuring the quality and efficiency of products in the industrial sector. While current Convolutional Neural Networks (CNNs) based on encoder-decoder architecture have achieved promising results for defect detection, they often struggle to capture long-range dependencies. In contrast, Transformer-based models recently demonstrated strong capabilities in global feature extraction but are limited to local features for the refined defect location. To address these limitations, this paper proposes a Label-Aware CNN-Transformer Network (LACTNet), which integrates explicit label-aware supervision and adaptive feature fusion for surface defect segmentation. The proposed model utilizes a dual-encoder architecture, employing convolutional layers to extract local features and the Swin Transformer to extract global features for surface defect segmentation.An adaptive feature fusion module (AFFM) is introduced to effectively aggregate the extracted features at each step of the encoder layers. Moreover, a multi-stage Label-Aware Module (LAM) is proposed to enhance the learning process by adding supervision in the decoder layers. We analyzed the performance of the proposed model using three public benchmark datasets. Based on our results, Dice scores of the LACTNet are as follows: 0.9328, 0.8339, 0.9757 for SD-saliency-900, NRSD-MN, and DAGM2007, respectively.