目的 针对目前表面缺陷检测方法因缺少实例级标签，使深度神经网络在工业检测上的应用受到限制的问题。本文面向实际的纸板表面缺陷检测任务，提出弱监督学习下融合卷积和注意力机制的神经网络算法。方法 该网络通过将通道注意力模块和梯度类激活映射模块相结合，进一步提高类激活图的精细度，实现纸板表面缺陷的精确定位;同时通过倒残缺结构和上采样层的组合操作，进一步细化浅层特征提升网络的特征提取能力，加快网络收敛速度。结果 通过在公开的纸板缺陷数据集上进行实验，本文提出的算法在使用图像级标签训练的情况下，分类正确率与定位正确率分别达到99.0%和92.2%，验证了该算法的有效性。结论 避免了实例级标签数量较少和过于主观的缺点，为基于机器人的缺陷纸板剔除奠定了基础。

The application of deep neural networks in industrial inspection is limited due to the lack of instance-level labels. To address this issue, the work aims to propose a neural network algorithm that combines convolution and attention mechanisms under weakly supervised learning for practical surface defect detection on cardboard. By integrating channel attention modules and gradient-based activation mapping modules, this network enhanced the precision of class activation maps and realized the precise localization of cardboard surface defects. Additionally, a combination of inverted residual structures and upsampling layers was utilized to refine shallow features and improve the network's feature extraction capabilities, thereby accelerating the convergence speed. Experiments were carried out on the publicly available cardboard defect dataset, achieving classification accuracy and localization accuracy of 99.0% and 92.2% respectively under the training with image-level labels and demonstrating the effectiveness of the proposed algorithm. The disadvantages of a small number of instance-level labels and excessive subjectivity are avoided, which lays a foundation for the removal of defective cardboard based on robots.