融合注意力机制的金属锅圆柱表面缺陷检测

Defect detection of cylindrical surface of metal pot combining attention mechanism

为实现高亮反射金属圆柱形锅的自动快速检测及分拣,破解目前金属锅表面缺陷检测速度慢、效率低的技术难题,在YOLOX网络基础上引入双向特征融合网络,提出基于注意力机制的轻量化特征融合网络模型,实现计算模型的轻量化设计;同时,通过注意力机制模块对特征信息进行通道与空间的学习,有效缓解多尺度特征的语义鸿沟问题,提高了模型的检测精度;考虑网络对难易分类样本学习权重分配不平衡,设计基于衰减因子的分类损失函数;利用金属锅圆柱表面缺陷数据集完成了特征融合网络对比实验、分类损失函数对比实验和注意力机制模块位置消融实验。实验结果表明,融合注意力机制模型可有效识别6种不同形态的缺陷,测试集的平均检测精度mAP0.5达到90.92%,检测帧率达到30.84 frame/s,实现了金属锅圆柱表面缺陷的高精度快速识别与定位。

To achieve the automatic and rapid detection and sorting of high-brightness reflection metal cylindrical pots,as well as break through the technical problems of slow speed and low efficiency of metal pot surface defect detection,a bi-directional feature pyramid network(BiFPN)was introduced in this study based on the YOLOX network.In addition,a lightweight feature fusion network model was devised on the basis of the attention mechanism,and the lightweight design of the computing model was realized.Meanwhile,the attention mechanism module was employed to learn the channel and space of feature information,effectively alleviating the semantic gap of multi-scale features and improving the detection precision of the model.Considering the unbalanced distribution of the learning weight of the network for difficult and easy classification samples,the classification loss function regarding the attenuation factor was determined.Comparisons of the feature fusion network,classification loss function,and attention mechanism module position ablation were conducted using the metal pot cylindrical surface defect dataset.The experimental results show that the fusion attention mechanism model can effectively identify six types of defects with different shapes,the average detection precision mAP0.5 of the test set realized 90.92%,and the detection frame rate was 30.84 FPS.Thus,cylindrical surface defects of metal pots can be identified and located,rapidly as well as with high precision,by using the proposed model.