Opto-Electronic Engineering, Volume. 51, Issue 1, 230292-1(2024)
Surface defect detection of solar cells using local and global feature fusion
Figures & Tables(15)
Fig. 7. Comparison chart of model accuracy compared to calculation amount and parameter amount
Fig. 8. Visual positioning results under YOLOv5 detection framework
Table 1. CViT-Net model parameter table
View tableView in ArticleTable 1. CViT-Net model parameter table
输入分辨率 模块 输出分辨率 CViT-Net-S CViT-Net-L 输出通道数 重复 输出通道数 重复 224 × 224 Inception 224 × 224 12 1 24 1 224 × 224 G-C2F 112 × 112 24 1 48 2 112 × 112 G-C2F 56 × 56 48 2 96 2 56 × 56 G-ViT 28 × 28 96 2 192 2 28 × 28 G-ViT 14 × 14 192 4 384 4 14 × 14 G-ViT 7 × 7 384 2 768 2 7 × 7 池化 1× 1 384 1 768 1 1 × 1 Conv2d 1 × 1 384 1 768 1 1 × 1 Conv2d 1 × 1 K 1 K 1 Parameter 5.6 M 21.9 M FLOPs 1.52 G 6.49 G
Table 2. Classify and detect experimentally different parameter values
View tableView in ArticleTable 2. Classify and detect experimentally different parameter values
名称 分类实验 检测实验 输入图像分辨率 224 × 224 640 × 640 训练轮数 (epoch) 100 300 批量尺寸 (Batch size) 40 8
Table 3. Comparison of advanced convolutional neural network algorithms
View tableView in ArticleTable 3. Comparison of advanced convolutional neural network algorithms
模型 测试分辨率 Precision/% Recall/% Accuracy/% Parameter/M FLOPs/G ResNet50 224×224 93.07 95.06 94.38 23.5 4.11 DenseNet121 224×224 90.03 93.78 91.60 6.9 2.86 EfficientNet-B0 224×224 92.33 94.11 93.60 5.3 0.39 RegNet 224×224 93.25 95.17 94.40 24.5 6.52 MobileVit 224×224 91.48 95.28 93.10 5.6 1.44 MobileNetV3 224×224 92.78 93.78 92.20 5.4 0.23 ShuffleNetV2 224×224 91.42 94.78 93.00 3.4 0.13 GhostNet 224×224 91.56 95.46 93.20 3.9 2.45 CViT-Net-S 224×224 93.00 95.94 94.50 5.6 1.52 CViT-Net-L 224×224 93.70 96.28 95.10 21.9 6.49
Table 4. Attention mechanism performance comparison
View tableView in ArticleTable 4. Attention mechanism performance comparison
模型 Precision/% Recall/% Accuracy/% Parameter/M FLOPs/G CViT-Net 79.86 80.25 86.78 4.59 4.2 CViT-Net+SE 80.74 80.58 87.68 5.60 1.53 CViT-Net+CBAM 87.0 87.6 92.24 5.63 1.54 CViT-Net+EMA 87.9 86.4 93.20 5.64 1.72 CViT-Net+CA 93.00 95.94 94.50 5.64 1.52
Table 5. CViT-Net-S network ablation experiment
View tableView in ArticleTable 5. CViT-Net-S network ablation experiment
模型 G-C2F G-ViT CA Parameter/M FLOPs/G Accuracy/% Baseline - - - 14.8 4.2 86.78 CViT-Net-S √ - - 8.9 1.35 92.15 CViT-Net-S √ √ - 12.6 1.51 92.31 CViT-Net-S √ √ √ 5.6 1.52 94.50
Table 6. Experimental comparison of different target detection algorithms
View tableView in ArticleTable 6. Experimental comparison of different target detection algorithms
模型 mAP/% mAP50/% 隐裂 暗斑 瑕疵 Two stage: Faster R-CNN( ResNet50) 86.1 85.4 76.8 82.8 Cascade:R-CNN( ResNet50) 89.3 86.8 79.9 85 Sparse R-CNN( ResNet50) 74.5 75.4 64.1 71.3 FoveaBox( ResNet50) 88.3 85.2 61.8 78.5 One stage: RetinaNet( ResNet50) 78.7 84.2 63.3 75.4 VFNet( ResNet50) 53.2 56.2 49.7 53 YOLOv5S 86.2 89.6 86.7 89.4 YOLOv6S 87.4 89.4 86.5 87.8 YOLOv7 80.8 86.7 81.2 82.9 YOLOv8S 86.9 88.1 86.0 87.0 YOLOX-S 88.1 88.8 87.2 88 PPYOLOE-S 87.7 89.9 84.5 87.4 YOLOv5(CViT-Net-S) 89.4 93.5 87.4 90.1 YOLOv5(CViT-Net-L) 89.5 93.6 87.5 90.2
Table 7. YOLOv5 backbone network comparison experiment
View tableView in ArticleTable 7. YOLOv5 backbone network comparison experiment
检测框架 骨干网络 Precision/% Recall/% mAP/% mAP50/% YOLOv5 ResNet50 83.9 85.1 48.4 86.3 YOLOv5 DenseNet121 83.2 83.9 49.0 88.1 YOLOv5 EfficientNet 87.0 84.9 52.1 89.3 YOLOv5 RegNet 86.3 85.5 52.8 88.9 YOLOv5 MobileVit 82.9 82.0 49.9 87.4 YOLOv5 MobileNetV3 89.5 86.5 58.3 89.8 YOLOv5 ShuffleNetV2 83.7 80.4 48.6 86.8 YOLOv5 GhostNet 85.6 86.3 52.8 89.3 YOLOv5 CViT-Net-S 87.2 87.1 56.2 90.1 YOLOv5 CViT-Net-L 90.1 87.3 61.1 90.2
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Zhiyong Tao, Yan He, Sen Lin, Tingjun Yi, Yaosheng Zhang. Surface defect detection of solar cells using local and global feature fusion[J]. Opto-Electronic Engineering, 2024, 51(1): 230292-1
Paper Information
Category: Article
Received: Dec. 1, 2023
Accepted: Feb. 2, 2024
Published Online: Apr. 19, 2024
The Author Email: Yan He (何燕)
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