Classification of Thermal Defects in Electronic Substrate Glass with ResNet34 Model Integrating CBAM Mechanism

doi:10.16552/j.cnki.issn1001-1625.2025.1071

Abstract

Abstract:

Electronic substrate glass is one of the key fundamental material in the information display industry. In recent years， as the information display industry has moved toward larger sizes， ultra-high definitions， and thinner， lighter designs， higher standards have been set for the quality of electronic substrate glass. In this paper， aiming at the challenges such as small size， high similarity， and difficult recognition of thermal defects in electronic substrate glass， the deep residual network model （ResNet34） was used as the main framework， the convolutional block attention module （CBAM） was integrated to improve the detection of minor target defects. The results show that， based on a homemade dataset of six typical thermal defects， the classification accuracy of the ResNet34 model integrating CBAM mechanism increases from 95.74% to 98.08%， with a notable improvement in generalization. Further visual analysis and comparisons reveal that the improved defect recognition performance with CBAM results from more precise defect localization. These findings offer a practical solution for online intelligent detection of thermal defects in electronic substrate glass and may serve as a reference for other minor target classification.

Key words: electronic substrate glass, glass manufacture, deep convolutional neural network, attention mechanism, thermal defect, defect classification

CLC Number:

TQ171

CAO Zhiqiang, LI Yuan, JIN Liangmao, YU Hao, CAO Xin, LIU Yong, HAN Gaorong. Classification of Thermal Defects in Electronic Substrate Glass with ResNet34 Model Integrating CBAM Mechanism[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2026, 45(3): 1074-1082.

Figures/Tables 8

Fig.1 Typical glass thermal defect images

Table 1 Configuration for deep residual network model ResNet34

Layer	Convolutional kernel	Number of channel	Number of stack	Output size of feature map/Pixel
Input	—	3	—	3×384×64
Pre-conv	$7 × 7$	64	1	64×192×32
Maxpool	$3 × 3$	64	1	64×192×32
ResNet-conv1	$3 × 3 3 × 3$	$6464$	3	64×96×16
ResNet-conv2	$3 × 3 3 × 3$	$128128$	4	128×48×8
ResNet-conv3	$3 × 3 3 × 3$	$256256$	6	256×24×4
ResNet-conv4	$3 × 3 3 × 3$	$512512$	3	512×12×2
Output	Avg-pool+flatten+fully connected+softmax			Class

Table 1 Configuration for deep residual network model ResNet34

Layer	Convolutional kernel	Number of channel	Number of stack	Output size of feature map/Pixel
Input	—	3	—	3×384×64
Pre-conv	$7 × 7$	64	1	64×192×32
Maxpool	$3 × 3$	64	1	64×192×32
ResNet-conv1	$3 × 3 3 × 3$	$6464$	3	64×96×16
ResNet-conv2	$3 × 3 3 × 3$	$128128$	4	128×48×8
ResNet-conv3	$3 × 3 3 × 3$	$256256$	6	256×24×4
ResNet-conv4	$3 × 3 3 × 3$	$512512$	3	512×12×2
Output	Avg-pool+flatten+fully connected+softmax			Class

Fig.2 Schematic diagram of ResNet34 structure integrating CBAM mechanism

Fig.3 Accuracy curves during model training process

Table 2 Model evaluation indicators for ablation experiments

Group	Test set	Training set				Model size/MB	Total training time/（h∶min∶s）
Group	Acc/%	Acc/%	Pr/%	Re/%	F1/%	Model size/MB	Total training time/（h∶min∶s）
A	85.80	95.74	94.60	94.58	94.59	123.46	1∶10∶12
B	88.41	96.50	96.70	96.67	96.68	129.03	1∶32∶46
C	91.84	97.51	97.97	97.92	97.94	141.64	1∶35∶40
D	93.56	98.08	98.35	98.33	98.34	141.64	1∶46∶05

Fig.4 Confusion matrix of classification results of different models on dataset

Fig.5 Gradient class activation mapping of tin defects in different models

Fig.6 Gradient class activation mapping of same defect in different models

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