Defect Detection Of Infrared Image Of Photovoltaic Cells Based On Convolutional Neural Network

The existence of photovoltaic cell defects will not only affect its power generation efficiency and product quality,but also reduce the safety of photovoltaic power generation.At present,the defect detection of battery cells relies more on manual inspection,which has the disadvantages of low efficiency,high work intensity and high cost.Therefore,it is of great significance and value to realize high-efficiency and high-precision crack detection of photovoltaic cells.Nowadays,the development of machine vision promotes the development of intelligent defect detection technology.Due to factors such as production processes and transportation methods,the background of the near-infrared image of photovoltaic cells is complex,and crack defects show multi-scale features with different shapes.As a result,traditional machine vision algorithms based on artificial design features are no longer more generic,and the detection accuracy is not high enough.Therefore,in this thesis,from the perspective of suppressing interference from complex backgrounds,reducing false detection to improve accuracy,and improving the model’s adaptability to multi-scale crack defect detection,three research-based detection algorithms are designed:In order to suppress the interference of the non-uniform texture complex background of the near infrared image on crack detection,a deep learning model Entropy-Dense Net based on the densely connected convolutional neural network(Dense Net)and information entropy data fusion strategy is proposed.The input during training is an image patch of 128×128pixels.Combining Dense Net with a sliding window enables crack detection of any highresolution image larger than the pixel size of the training set.The model makes full use of the spatial structure of the crack and combines with the information entropy formula to determine whether the target belongs to a real crack,so as to complete the structural prediction centered on the input image patch.Experimental results show that the proposed model effectively reduces the false detection of crack defects and improves the accuracy.However,because the test image and the crack have different sizes and proportions,it is difficult for the model to find the optimal size of the sliding window and the speed of EntropyDense Net’s evaluation of the image depends on the size of the crack,so the performance needs to be further improved.In order to directly obtain the global feature information of high-resolution near-infrared images,a Faster-RCNN target detection model with attention CBAM is proposed to achieve crack defect detection for 1024×1024 pixel images.The model uses VGG as a feature extraction network,and the convolutional attention module CBAM is applied to the RPN network to increase the model’s attention to crack defects and obtain a more robust feature map.Experimental results show that the performance of CBAM-fused Faster-RCNN is higher than that of the original detection model,and it is better than Faster-RCNN that only uses channel attention or spatial attention.However,there is a missed detection of smallsized cracks in a complex background.In order to improve the adaptability of the model to multi-scale crack defects,this thesis proposes a multi-scale Faster-RCNN target detection model based on the above results.The model combines the deeper residual network Res Net50 with the improved path aggregation feature pyramid network PA-FPN.It is through multi-layer feature fusion to obtain highresolution,multi-scale feature maps containing rich semantic information,which can improve the model’s ability to express features of small targets in a complex background.At the same time,the RPN network uses the loss function Focal loss 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.In addition,the clustering algorithm k-means guides the RPN to set the anchor closer to the actual crack size,which is beneficial to the position regression of the target box.Experimental results show that the proposed model achieves excellent multi-scale crack detection effect.In addition,this thesis has conducted experiments based on thermal infrared image data sets of photovoltaic power plant inspection.It is verified that the multi-scale Faster RCNN model can achieve efficient and accurate detection of small target defects,and has good robustness and generalization ability.