融合先验知识推理的表面缺陷检测

Fusing prior knowledge reasoning for surface defect detection

当前基于深度学习的表面缺陷检测方法主要侧重于单独识别缺陷实例,即仅从区域特征方面考虑缺陷检测。然而,这种方法忽略了缺陷之间的高层关系,难免会出现缺陷检测误差。针对上述问题,提出了一种融合先验知识推理的表面缺陷检测网络(PKR-Net)。PKR-Net主要由2个部分组成,即显性知识推理模块(EKRM)和隐性知识推理模块(IKRM)。EKRM通过构建显性关系图(ERG)来捕获数据集中缺陷之间的全局共现关系得到共现关系特征,而IKRM通过构建隐性关系图(IRG)来捕获图像中缺陷之间的局部空间关系得到空间关系特征。最后将得到的共现关系特征和空间关系特征进行融合,并重新送入分类层和回归层以改进检测效果。在工业缺陷数据集Textile,NEU-DET和GC10-DET上进行实验验证,实验结果表明,该网络模型相比基线模型Faster RCNN,其mAP分别提升了14.8%,8.2%和18.9%,与其他缺陷检测模型相比能够达到更好的检测性能,验证了模型的有效性。

Current surface defect detection methods based on deep learning mainly focus on the individual identification of defect instances,considering defect detection only from the aspect of region features.However,this method overlooks the high-level relation between defects,which will inevitably lead to defect detection errors.To address the above problems,a surface defect detection network(PKR-Net)based on prior knowledge reasoning was proposed.Specifically,PKR-Net mainly consists of two parts,namely,the explicit knowledge reasoning module(EKRM)and the implicit knowledge reasoning module(IKRM).EKRM constructed an explicit relation graph(ERG)to capture the global co-occurrence relation between defects in the dataset,thereby obtaining co-occurrence relation features.Meanwhile,IKRM constructed an implicit relation graph(IRG)to capture the local spatial relation between defects in the image,thereby obtaining spatial relation features.Finally,the co-occurrence relation features and spatial relation features were fused and re-fed into the classification and regression layers to improve detection performance.Experimental verification was conducted on the industrial defect datasets Textile,NEU-DET and GC10-DET.The experimental results showed that the mAP of the proposed network model improved by 14.8%,8.2%,and 18.9%,respectively,compared with the baseline model Faster RCNN.Compared with other defect detection models,the proposed model can achieve better detection performance,verifying its effectiveness.