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• This paper proposes an innovative approach that integrates neural networks with photoluminescence detection technology to address defects such as cracks, dirt, dark spots, and scratches in solar cells. • The YOLOv5 model undergoes optimization in three distinct stages, encompassing global optimization, neck network structure refinement, and head structure enhancement. These optimizations collectively elevate the model's capabilities in recognizing, detecting, and classifying defects in solar cells. • Advanced techniques are employed, including the introduction of structures such as Transformer Encoder, the incorporation of additional small target detection boxes, utilization of Bi-FPN structure, and insertion of CBAM structure blocks. These optimizations contribute to the improvement of the model's feature recognition and extraction capabilities, further enhancing its ability to identify defects. In the production process of solar cells, inevitable faults such as cracks, dirt, dark spots, and scratches may occur, which could potentially impact the lifespan and power generation efficiency of solar cells. Addressing this issue, this paper combines neural networks with photoluminescence detection technology and proposes a novel neural network model for the classification and grading of defects in solar cells. Firstly, the YOLOv5 model is optimized and adjusted for algorithm and network structure. The optimization process is divided into three parts: global optimization of the network structure, optimization of the neck network structure, and optimization of the head structure, each addressing specific issues in recognition, detection, and classification. The impact of the optimized network model on recognition and detection speed is analyzed, and solutions are proposed to address any observed effects. Additionally, an iterative update of neural network hyperparameter combinations is performed for solar cell defect identification. Finally, using the ultimately optimized model structure in conjunction with the optimal hyperparameter combination, comparative experiments are conducted on neural networks for different target identification using the photoluminescence characteristics dataset of solar cells. The recognition improvement of the optimized model and its differences from other models are analyzed. [ABSTRACT FROM AUTHOR]

• A new non-destructive detection method that integrates a convolutional neural network and high-power halogen lamp-induced thermal-wave diffusion multidimensional features were introduced to realize the intelligent classification and quantitative characterization of honeycomb sandwich composites (HSCs) defects. • YOLOLIT is designed to achieve intelligent classification and identification of defects and quantitative characterization of defect sizes. • The optimal hyperparameter combination is obtained by iterating the hyperparameters of the neural network using a genetic algorithm. HSCs defects may have a serious impact on the mechanical properties of the material. Accurate and reliable non-destructive defect detection of materials can reduce safety hazards. Therefore, a reliable technology for HSCs defect detection is needed. In this present study, a new non-destructive detection method that integrates a convolutional neural network (CNN) and high-power halogen lamp-induced thermal-wave diffusion multidimensional features were introduced to realize the intelligent classification and quantitative characterization of honeycomb sandwich composites (HSCs) defects. Firstly, HSC specimens with delamination defects were detected by the halogen lamp-induced lock-in thermography (LIT) algorithm, and feature images were extracted. After image pre-processing, the images are used as defect feature datasets. Furthermore, combining the four-layer feature pyramid structure and Transformer, a CNN model named YOLOLIT is designed to achieve intelligent classification and identification of defects and quantitative characterization of defect sizes. Subsequently, the optimal hyperparameter combination is obtained by iterating the hyperparameters of the neural network using a genetic algorithm. In a complex environment, accurate recognition of circular defects with a minimum diameter of Φ6mm is achieved, and the mAP of up defect is 90.2%, down defect is 86.2%, remove defect is 72.9%, and the average recognition speed of 0.2 s/image is achieved. Finally, the relation between defect size and detection error as well as that between LIT frequency and detection error is studied. The detection error range and optimal LIT frequency are obtained. The experimental results illustrated this method may be a potential infrared non-destructive defect detection solution. [ABSTRACT FROM AUTHOR]