MCTNet: Defect Detection in Aluminum Alloy Die-Casting Using a Multilevel CNN-Transformer Interaction Network

Aluminum alloy die-castings hold a critical strategic position in industries such as automotive manufacturing, aerospace, and rail transportation. In their visual defect detection, defects exhibiting complex backgrounds, multiscale variations, and irregular shapes challenge existing detection models. To tackle these challenges, this article proposes the Multi-Level CNN-Transformer Interaction Network (MCTNet), which integrates the convolutional neural network’s (CNNs) capacity for local detail analysis with the Transformer’s strength in capturing global information. To address the distinct characteristics of the two branches, we propose two modules: channel attention down-sampling (CADS) and cross global attention (CGA). These modules enable multilevel interaction and fusion, overcoming single-module limitations. We then design a super-resolution-enhanced feature fusion structure to enhance fine defect feature extraction. Finally, an adaptive feature weighting (AFW) and task-aligned detection head is proposed to synergize defect localization and classification. The proposed method outperforms the baseline model by improving mAP₅₀ and mAP ${}_{50\text {:}90}$ by 4.8% and 5.7%, respectively, while surpassing state-of-the-art CNN-based and Transformer-based models in accuracy.