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融合注意力机制与联合优化的表面缺陷检测

Attention Mechanism Based Joint Optimization Algorithm for Defect Detection

  • 摘要: 两段式缺陷检测模型中分割和分类网络的优化目标不一致, 导致二者耦合性较差, 且分割模块误差的积累可能进一步弱化分类模块的性能. 针对上述问题, 提出一种基于注意力机制的缺陷检测联合优化模型. 首先通过基于混合注意力特征融合模块的分割网络融合浅层特征和深层特征, 提取更全面的缺陷位置信息; 然后通过基于多感受野空间注意力模块的分类网络挖掘更具判别性的缺陷类别特征; 最后通过联合优化目标实现分割和分类网络的联合优化, 提升整个模型的耦合性, 从而提升模型的性能. 基于PyTorch 框架在公开工业缺陷检测数据集DAGM 2007, MAGNETIC-TILE和KolektorSDD2数据集上进行验证实验, 并引入分段式算法及类U-Net算法进行横向对比. 实验表明, 该算法的准确率相比分段式算法最高提升28.02%, 相比类U-Net算法最高提升8.3%, 且精确率、召回率、值均优于同类算法, 具有更好的检测性能.

     

    Abstract: The objectives of segmentation network and classification network in two-step defect detection model are inconsistent, resulting in the low coupling between them, and the error accumulated in segmentation network further weakens the classification network performance. To address these problems, a joint optimization model for defect detection is proposed, named MADD-Net, which can simultaneously predict both the location and category of defects based on the attention mechanism. Firstly, the segmentation network fuses the shallow and deep features to extract more information based on the mixed attention feature fusion module. Then, the classification network captures more discriminative features based on the multi-receptive-field spatial attention module. Finally, the segmentation and classification networks are trained simultaneously via the joint optimization objective. Extensive experiments are conducted on various public industrial defect detection datasets (DAGM 2007, MAGNETIC-TILE, and KolektorSDD2) based on PyTorch framework, and the proposed method achieves superior performance. The accuracy of this algorithm is up to 28.02% higher than that of piece-wise algorithm and 8.3% higher than that of U-Net-like algorithm. The precision, recall and -score are also better than other state-of-the-art models, which has better detection performance.

     

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