Surface defect inspection is an important task in industrial inspection. Deep learning-based methods have demonstrated promising performance in this domain. Nevertheless, these methods still suffer from misjudgment when encountering challenges such as low-contrast defects and complex backgrounds. To overcome these issues, we present a decision fusion network (DFNet) that incorporates the semantic decision with the feature decision to strengthen the decision ability of the network. In particular, we introduce a decision fusion module (DFM) that extracts a semantic vector from the semantic decision branch and a feature vector for the feature decision branch and fuses them to make the final classification decision. In addition, we propose a perception fine-tuning module (PFM) that fine-tunes the foreground and background during the segmentation stage. PFM generates the semantic and feature outputs that are sent to the classification decision stage. Furthermore, we present an inner-outer separation weight matrix to address the impact of label edge uncertainty during segmentation supervision. Our experimental results on the publicly available datasets including KolektorSDD2 (96.1% AP) and Magnetic-tile-defect-datasets (94.6% mAP) demonstrate the effectiveness of the proposed method.

翻译：表面缺陷检测是工业检测中的一项重要任务。基于深度学习的方法在该领域已展现出令人期待的性能。然而，当面对低对比度缺陷和复杂背景等挑战时，这些方法仍存在误判问题。为解决这些问题，我们提出了一种决策融合网络（DFNet），该网络将语义决策与特征决策相结合以增强网络的决策能力。具体而言，我们引入了一个决策融合模块（DFM），该模块从语义决策分支提取语义向量，从特征决策分支提取特征向量，并将两者融合以做出最终分类决策。此外，我们提出了一个感知微调模块（PFM），该模块在分割阶段对前景和背景进行微调。PFM生成语义输出和特征输出，并将其送入分类决策阶段。同时，我们引入了一个内外分离权重矩阵，以解决分割监督过程中标签边缘不确定性的影响。在公开数据集（包括KolektorSDD2和Magnetic-tile-defect-datasets）上的实验结果表明，该方法取得了有效性，分别达到了96.1%的平均精度（AP）和94.6%的平均精度均值（mAP）。