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摘要
电致发光(Electroluminescence, EL)下的光伏电池EL图像背景表现为复杂的非均匀纹理特征，且存在与裂纹相似的晶粒伪缺陷，同时裂纹表现为形状多样的多尺度特征，以上难点为检测任务带来了极大的挑战。因此，本文提出融合注意力的多尺度Faster-RCNN模型，一方面，采用改进的特征金字塔网络获取多尺度的高级语义特征图，以此来提高网络对多尺度裂纹缺陷的特征表达能力。另一方面，采用改进的注意力区域推荐网络A-RPN，提高模型对裂纹缺陷的关注并抑制复杂背景及晶粒伪缺陷的特征。同时，在RPN网络训练过程中，采用损失函数Focal loss，以此来降低训练过程中简单样本所占比重，使其更加关注难以区分的样本。实验结果表明，改进的算法使得EL图像裂纹缺陷检测的准确率提高，达到接近95%。
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关键词:
- 多尺度特征提取 /
- 注意力模块 /
- Focal loss函数
Abstract
The background of the EL image of a photovoltaic cell under electroluminescence (EL) presents complex non-uniform texture features, and there are grain pseudo-defects similar to cracks. At the same time, the cracks appear as multi-scale features with various shapes. The above mentioned difficulties have presented great challenges for the detection task. Therefore, this paper proposes a multi-scale Faster-RCNN model that integrates attention. On the one hand, an improved feature pyramid network is used to obtain multi-scale advanced semantic feature maps to improve the network's feature expression ability of multi-scale crack defects. On the other hand, an improved attention region proposal network A-RPN is adopted to increase the model's attention to crack defects and suppress the characteristics of complex background and grain pseudo-defects. At the same time, in the RPN network training process, a loss function Focal loss is used to reduce the proportion of simple samples in the training process, so that the model pays more attention to the samples that are difficult to distinguish. Experimental results show that this algorithm improves the accuracy of crack defect detection in EL images, reaching nearly 95%.
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Key words:
- multi-scale feature extraction /
- attention module /
- focal loss function
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Overview
Overview: Electroluminescence (EL) images of photovoltaic cells have a non-uniformly textured complex background, and the background contains grain pseudo-defects that are highly similar to the crack structure. At the same time, the cracks are characterized by various sizes and shapes. Existing target detection algorithms based on convolutional neural networks cannot adapt to the above problems. From the perspective of suppressing interference from complex background and improving the adaptability of the model to multi-scale crack defect detection, this paper proposes a multi-scale Faster RCNN model that integrates attention. In photovoltaic cell EL images, the scale of the cracks varies greatly, including a large number of small target cracks. In order to improve the network's ability to express multi-scale crack defects, a path aggregation feature pyramid network (PA-FPN) is proposed. Based on the combination of the residual network ResNet50 and the feature pyramid network FPN, PA-FPN adds a bottom-up path to fuse features. PA-FPN effectively retains shallow feature information, which improves the model's adaptability to multi-scale cracks in EL images and especially the detection results of small-scale cracks. In order to improve the model's attention to crack defects and suppress the characteristics of complex background and grain pseudo-defects, this paper proposes a regional recommendation network A-RPN that incorporates convolutional block attention module (CBAM). CBAM is composed of a channel attention module and a spatial attention module. In this paper, it is experimentally verified that the detection result of the RPN network fused with CBAM is better than that of using an attention modules alone. K-means clustering is used to cluster the crack sizes in the data set to guide the RPN to set the anchor box closer to the actual crack size, which improves the speed and accuracy of the target box regression in the defect detection process. In addition, in the RPN network training process, the loss function Focal loss is used to replace the original cross-entropy loss function, so as to reduce the proportion of simple samples in the training process and make the model pay more attention to the samples that are difficult to distinguish. The entire network can achieve end-to-end training. In order to verify the effectiveness of the improved algorithm, the performance of the original Faster RCNN model, RetinaNet, and CenterNet on multi-scale crack detection of EL images is compared. Through training and testing of 1024 pixels×1024 pixels of photovoltaic cell EL images, experimental results show that the improved Faster RCNN is better than the above mentioned target detection algorithms in accuracy, and has good robustness to the strip-shaped multi-scale cracks, which can be adapted to the EL image with changing complex background.
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图 2 非均匀纹理随机背景的EL图像。
Figure 2. EL image of a random background with a non-uniform texture. The rectangular frame is the grain, the triangular frame marks the pseudo-defects of the grain that are highly similar to the crack, and the ellipse marks the crack
图 7 RPN结合注意力CBAM前后的特征图
Figure 7. Feature map before and after RPN combined with attention CBAM
图 8 不同算法在光伏电池EL图像上的检测结果对比图
Figure 8. Comparison of detection results of different algorithms on photovoltaic cell EL images
表 1 光伏电池EL图像数据集
Table 1. Photovoltaic cell EL image data set
分辨率 训练集 测试集 合计 1024×1024 476 236 712 
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表 2 模型的参数配置
Table 2. Parameter configuration of the model
Image_resize Weight_decay Learning_rate Network_batch_size 1024×1024 0.0005 0.0001 1 Momentum RPN_proposals_train RPN_proposals_test RPN batch_size 0.9 2000 1000 256 Max_iteration ROI_foreground threshold ROI_background threshold RPN_nms threshold 20000 (0.7, 1) (0, 0.3) 0.7
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表 3 基于Faster-RCNN算法的EL图像检测性能
Table 3. EL image detection performance based on Faster-RCNN algorithm
Faster-RCNN Focal loss 注意力 PA-FPN AP ResNet50 - - - 87.68 √ - - 88.93 √ √ - 92.26 √ √ √ 94.75
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