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<p>Automated defect detection in mechanical castings is critical for ensuring product quality in modern manufacturing. However, existing methods struggle to simultaneously achieve micro-defect sensitivity, global contextual understanding, and computational efficiency. In this paper, we propose CastMamba, a novel encoder–decoder network that integrates selective state-space modeling with multi-scale attention mechanisms for high-precision casting defect detection. The encoder employs a micro-defect attention module (MDAM) that leverages multi-scale dilated convolutions with a Bilinear Attention Module to capture fine-grained defect features across varying scales. A state-space global context module (SSGCM) serves as the bottleneck, harnessing the selective state-space mechanism to model long-range spatial dependencies with linear computational complexity. A local-global dual-branch fusion module (LGDFM) further enriches feature representations through patch-based attention at multiple granularities. The decoder utilizes a global-local feature decoding module (GLFDM) that combines dilated convolutions with vision transformers for comprehensive feature reconstruction. Extensive experiments on three benchmarks demonstrate that CastMamba achieves state-of-the-art performance, attaining 88.4% mAP@50 on MCDD, 91.2% mAP@50 on BDD, and 99.4% image-level AUROC on MVTec-AD, consistently surpassing seventeen competitive baselines. The source code is publicly available at <a href="https://github.com/wfcyliyuheng-dev/CastMamba">https://github.com/wfcyliyuheng-dev/CastMamba</a>.</p>

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Efficient mechanical casting defect detection based on state space modeling and multiscale attention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • HaoQiong Wu,
  • Yuheng Li

摘要

Automated defect detection in mechanical castings is critical for ensuring product quality in modern manufacturing. However, existing methods struggle to simultaneously achieve micro-defect sensitivity, global contextual understanding, and computational efficiency. In this paper, we propose CastMamba, a novel encoder–decoder network that integrates selective state-space modeling with multi-scale attention mechanisms for high-precision casting defect detection. The encoder employs a micro-defect attention module (MDAM) that leverages multi-scale dilated convolutions with a Bilinear Attention Module to capture fine-grained defect features across varying scales. A state-space global context module (SSGCM) serves as the bottleneck, harnessing the selective state-space mechanism to model long-range spatial dependencies with linear computational complexity. A local-global dual-branch fusion module (LGDFM) further enriches feature representations through patch-based attention at multiple granularities. The decoder utilizes a global-local feature decoding module (GLFDM) that combines dilated convolutions with vision transformers for comprehensive feature reconstruction. Extensive experiments on three benchmarks demonstrate that CastMamba achieves state-of-the-art performance, attaining 88.4% mAP@50 on MCDD, 91.2% mAP@50 on BDD, and 99.4% image-level AUROC on MVTec-AD, consistently surpassing seventeen competitive baselines. The source code is publicly available at https://github.com/wfcyliyuheng-dev/CastMamba.