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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.
EL imaging acquisition system
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
Multi-scale Faster-RCNN model with attention
Path aggregation feature pyramid PA-FPN
Detection model with integrated CBAM
Visual comparison of feature maps
Feature map before and after RPN combined with attention CBAM
Comparison of detection results of different algorithms on photovoltaic cell EL images