融合先验知识推理的表面缺陷检测

doi:10.11996/JG.j.2095-302X.2024050957

图学学报 ›› 2024, Vol. 45 ›› Issue (5): 957-967.DOI: 10.11996/JG.j.2095-302X.2024050957

• 图像处理与计算机视觉 • 上一篇下一篇

融合先验知识推理的表面缺陷检测

姜晓恒¹^,²^,³(), 段金忠¹, 卢洋¹^,²^,³(), 崔丽莎¹^,²^,³, 徐明亮¹^,²^,³

1.郑州大学计算机与人工智能学院，河南郑州 450001
2.智能集群系统教育部工程研究中心，河南郑州 450001
3.国家超级计算郑州中心，河南郑州 450001

收稿日期:2024-07-04 修回日期:2024-07-17 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者:卢洋(1991-)，女，讲师，博士。主要研究方向为计算机视觉和目标检测。E-mail：ieylu@zzu.edu.cn
第一作者:姜晓恒(1985-)，男，教授，博士。主要研究方向为计算机视觉和深度学习。E-mail：jiangxiaoheng@zzu.edu.cn
基金资助:
国家重点研发计划项目(2021YFB3301500);国家自然科学基金项目(62172371);国家自然科学基金项目(U21B2037);国家自然科学基金项目(62102370);国家自然科学基金项目(62106232);河南省自然科学基金项目(232300421093)

Fusing prior knowledge reasoning for surface defect detection

JIANG Xiaoheng¹^,²^,³(), DUAN Jinzhong¹, LU Yang¹^,²^,³(), CUI Lisha¹^,²^,³, XU Mingliang¹^,²^,³

1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou Henan 450001, China
2. Engineering Research Center of Intelligent Swarm Systems, Ministry of Education, Zhengzhou Henan 450001, China
3. National Supercomputing Center in Zhengzhou, Zhengzhou Henan 450001, China

Received:2024-07-04 Revised:2024-07-17 Published:2024-10-31 Online:2024-10-31
Contact: LU Yang (1991-), lecturer, Ph.D. Her main research interests cover computer vision and object detection. E-mail：ieylu@zzu.edu.cn
First author：JIANG Xiaoheng (1985-), professor, Ph.D. His main research interests cover computer vision and deep learning. E-mail：jiangxiaoheng@zzu.edu.cn
Supported by:
National Key Research and Development Program of China(2021YFB3301500);National Natural Science Foundation of China(62172371);National Natural Science Foundation of China(U21B2037);National Natural Science Foundation of China(62102370);National Natural Science Foundation of China(62106232);Natural Science Foundation of Henan Province of China(232300421093)

摘要/Abstract

摘要：

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。

关键词: 表面缺陷检测, 先验知识, 图卷积网络, 目标检测, 深度学习

Abstract:

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.

Key words: surface defect detection, prior knowledge, graph convolutional network, object detection, deep learning

中图分类号:

姜晓恒, 段金忠, 卢洋, 崔丽莎, 徐明亮. 融合先验知识推理的表面缺陷检测[J]. 图学学报, 2024, 45(5): 957-967.

JIANG Xiaoheng, DUAN Jinzhong, LU Yang, CUI Lisha, XU Mingliang. Fusing prior knowledge reasoning for surface defect detection[J]. Journal of Graphics, 2024, 45(5): 957-967.

图/表 15

图1 PKR-Net模型结构图

Fig. 1 Structure of the PKR-Net model

图2 显性知识推理模块

Fig. 2 Explicit knowledge reasoning module

图3 共现频数矩阵

Fig. 3 Co-occurrence frequency matrix

图4 共现关系矩阵

Fig. 4 Co-occurrence relation matrix

图5 隐性知识推理模块

Fig. 5 Implicit knowledge reasoning module

表1 不同缺陷数据集上各模型评估结果对比

Table 1 Comparison of evaluation results of each model on different defect datasets

模型	主干网络	Textile数据集/%			NEU-DET数据集/%			GC10-DET数据集/%
模型	主干网络	mAP	AP₅₀	AP₇₅	mAP	AP₅₀	AP₇₅	mAP	AP₅₀	AP₇₅
SSD512	VGG16	37.9	71.5	30.9	31.2	67.5	24.3	29.2	59.9	26.7
Faster RCNN	ResNet101	41.2	76.4	34.9	36.6	72.7	32.9	28.5	62.3	23.1
FCOS	ResNet101	39.9	73.4	35.3	37.1	70.2	34.0	30.5	62.3	25.6
Sparse RCNN	ResNet101	39.9	67.9	38.8	38.1	69.0	37.3	28.9	59.7	24.6
RetinaNet	ResNet101	40.7	74.2	34.9	36.0	73.5	30.3	30.0	59.6	28.4
Cascade RCNN	ResNet101	46.2	79.0	44.4	38.7	73.5	36.6	32.5	65.5	30.2
YOLOX-s	CSPDarkNet53	46.5	79.5	41.9	37.7	71.7	34.7	31.3	63.3	29.8
Reasoning RCNN	ResNet101	45.5	78.4	40.7	38.2	73.5	36.6	31.2	65.8	27.2
LF-YOLO	-	39.0	71.2	38.0	33.6	68.3	28.4	31.0	67.0	26.1
CANet	ResNet101	41.1	73.8	39.4	38.2	72.6	32.2	30.6	63.5	26.1
PKR-Net	ResNet101	47.3	80.2	45.6	39.6	76.4	35.6	33.9	67.4	30.7

图6 不同缺陷数据集上各模型精确率-召回率曲线对比

Fig. 6 Comparison of precision-recall curves of each model on different defect datasets ((a) Textile dataset; (b) NEU-DET dataset; (c) GC10-DET dataset)

表2 不同数据集上各模型F1指标对比/%

Table 2 Comparison of F1 metrics of different models on different datasets /%

模型	Textile 数据集	NEU-DET 数据集	GC10-DET 数据集
SSD512	71.1	68.2	57.5
Faster RCNN	70.0	71.2	58.5
FCOS	69.1	70.5	62.7
Sparse RCNN	64.7	67.6	57.1
RetinaNet	67.0	70.7	61.2
Cascade RCNN	74.0	72.7	62.3
YOLOX-s	76.6	69.7	64.1
Reasoning RCNN	74.5	70.0	60.4
LF-YOLO	67.4	65.4	64.0
CANet	68.6	71.7	62.2
PKR-Net	77.3	74.7	64.8

表3 Textile数据集上不同损失权重消融实验结果

Table 3 Ablation experiment results of different loss weights on Textile dataset

φ	γ	mAP/%	AP₅₀/%
1.00	1.00	45.6	78.6
1.00	0.75	46.8	79.5
1.00	0.50	47.3	80.2
1.00	0.25	45.5	78.1

表4 Textile数据集上不同模块下的消融实验结果

Table 4 Ablation experiment results under different modules on Textile dataset

模型	EKRM	IKRM	mAP/%	AP₅₀/%
Faster RCNN	-	-	41.2	76.4
PKR-Net	✓	-	44.6	76.8
PKR-Net	-	✓	45.0	77.0
PKR-Net	✓	✓	47.3	80.2

表5 Textile数据集上不同知识下的消融实验结果

Table 5 Ablation experiment results under different knowledge on Textile dataset

模块	知识形式	mAP/%
EKRM	全0	41.8
	全1	42.3
	随机	42.1
	正常	44.6
IKRM	全0	41.7
	全1	42.4
	随机	42.3
	正常	45.0

图7 不同缺陷数据集上基线模型与PKR-Net模型检测误差对比

Fig. 7 Comparison of detection errors between baseline and PKR-Net on different defect datasets ((a) Textile dataset; (b) NEU-DET dataset; (c) GC10-DET dataset; (d) Textile dataset; (e) NEU-DET dataset; (f) GC10-DET dataset)

图8 不同方法在Textile数据集上的检测结果对比

Fig. 8 Comparison of detection results of different methods on Textile dataset ((a) Ground Truth; (b) SSD512; (c) Faster RCNN; (d) FCOS; (e) Sparse RCNN; (f) RetinaNet; (g) Cascade RCNN; (h) YOLOx-s; (i) Reasoning RCNN; (j) LF-YOLO; (k) CANet; (l) PKR-Net)

图9 不同方法在NEU-DET数据集上的检测结果对比

Fig. 9 Comparison of detection results of different methods on NEU-DET dataset ((a) Ground Truth; (b) SSD512; (c) Faster RCNN; (d) FCOS; (e) Sparse RCNN; (f) RetinaNet; (g) Cascade RCNN; (h) YOLOx-s; (i) Reasoning RCNN; (j) LF-YOLO; (k) CANet; (l) PKR-Net)

图10 不同方法在GC10-DET数据集上的检测结果对比

Fig. 10 Comparison of detection results of different methods on GC10-DET dataset ((a) Ground Truth; (b) SSD512; (c) Faster RCNN; (d) FCOS; (e) Sparse RCNN; (f) RetinaNet; (g) Cascade RCNN; (h) YOLOx-s; (i) Reasoning RCNN; (j) LF-YOLO; (k) CANet; (l) PKR-Net)

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融合先验知识推理的表面缺陷检测

Fusing prior knowledge reasoning for surface defect detection

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