The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and processed by an advanced lightweight convolutional neural network (CNN). Firstly, in order to solve the high pixel SCC image detection, each silicon wafer image was segmented based on local difference extremum of edge projection (LDEEP). Secondly, in order to detect the defects with small size or weak edges in the silicon wafer, an improved lightweight CNN model with deep backbone feature extraction network structure was proposed, as the enhancing feature fusion layer and the three-scale feature prediction layer; the model provided more feature detail. The final experimental results showed that the improved model achieves a good balance between the detection accuracy and detection speed, with the mean average precision (mAP) reaching 87.55%, which was 6.78% higher than the original algorithm. Moreover, the detection speed reached 40 frames per second (fps), which meets requirements of precision and real-time detection. The detection method can better complete the defect detection task of SCC, which lays the foundation for automatic detection of SCC defects.

中文翻译：

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基于轻量级卷积神经网络的太阳能电池组件在线缺陷检测方法研究

太阳能电池组件（SCC）的缺陷会影响使用寿命和发电效率。在本文中，SCC 的缺陷图像是通过光致发光 (PL) 方法拍摄的，并由先进的轻量级卷积神经网络 (CNN) 处理。首先，为了解决高像素SCC图像检测，基于边缘投影的局部差分极值（LDEEP）对每个硅片图像进行分割。其次，为了检测硅片中尺寸小或边缘弱的缺陷，提出了一种改进的具有深度主干特征提取网络结构的轻量级CNN模型，作为增强特征融合层和三尺度特征预测层；该模型提供了更多的特征细节。最终的实验结果表明，改进后的模型在检测精度和检测速度之间取得了很好的平衡，平均精度（mAP）达到了87.55%，比原算法提高了6.78%。并且检测速度达到了每秒40帧（fps），满足了检测精度和实时性的要求。该检测方法可以更好地完成SCC的缺陷检测任务，为SCC缺陷的自动检测奠定了基础。