Al-Waisy Alaa S, Ibrahim Dheyaa, Zebari Dilovan Asaad, Hammadi Shumoos, Mohammed Hussam, Mohammed Mazin Abed, Damaševičius Robertas
Computer Engineering Techniques Department, Information Technology College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq.
Department of Computer Science, College of Science, Nawroz University, Duhok, Kurdistan Region, Iraq.
PeerJ Comput Sci. 2022 May 19;8:e992. doi: 10.7717/peerj-cs.992. eCollection 2022.
Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and requires deep expert knowledge. In this work, a hybrid and fully-automated classification system is developed for detecting different types of defects in EL images. The system fuses the deep feature representations extracted from two different deep learning models (Inception-V3 and ResNet50) to form more discriminative feature vectors. These feature vectors are then fed into the classifier layer to assign them into one of different types of defects. A large-scale, challenging solar cells dataset composed of 2,624 EL images was used to assess the performance of the proposed system in both the binary classification (functional vs defective) task and multi-class classification (functional, mild, moderate, and severe) task. The proposed system has managed to detect the correct defect type with less than 1 s per image with an accuracy rate of 98.15% and 95.35% in the binary classification and multi-classification task, respectively.
电致发光（EL）成像是一种获取光伏（PV）模块图像并检查其表面缺陷的技术。对EL图像的分析一直是由专家通过目视检查手动进行的。这种手动程序繁琐、耗时、主观，并且需要深厚的专业知识。在这项工作中，开发了一种混合全自动分类系统，用于检测EL图像中的不同类型缺陷。该系统融合了从两种不同深度学习模型（Inception-V3和ResNet50）提取的深度特征表示，以形成更具判别力的特征向量。然后将这些特征向量输入到分类器层，将它们分配到不同类型的缺陷之一。使用一个由2624张EL图像组成的大规模、具有挑战性的太阳能电池数据集，来评估所提出系统在二分类（功能正常与有缺陷）任务和多分类（功能正常、轻度、中度和重度）任务中的性能。所提出的系统在二分类任务和多分类任务中分别以每张图像不到1秒的时间检测出正确的缺陷类型，准确率分别为98.15%和95.35%。
