Solar energy, in the form of photovoltaic (PV) panels, is important for achieving clean energy solutions. The photovoltaic health index must be monitored and improved because of the high demand for green energy. Unfortunately, defective solar cells are a significant source of performance degradation in photovoltaic (PV) systems. Experts often manually analyze electroluminescence (EL) images by visually inspecting them, which is personal, time-consuming, and requires extensive expertise. This work presents a comparative analysis of YOLOv8 and an Improved YOLOv5 for an automatic PV defect detection system in EL images in which Global Attention Module (GAM) is incorporated into the traditional YOLOv5s model for better object representation. Adaptive Feature space fusion (ASFF) was added to YOLOv5’s original structure for feature fusion. The Distance Intersection over Union (Non-Maximum) Suppression (DIoU-NMS) is aggregated to produce a more accurate bounding box. The ELDDS1400C5 dataset was used to train and evaluate the proposed system. Experiments on the ELDDS1400C5 test set revealed that the Improved YOLOv5 algorithm achieved a mean Average Precision of 76.3% (mAP@0.5), which is a 2.5% improvement over the standard YOLOv5 algorithm for detecting faults in PV modules in EL images. Furthermore, the experimental results demonstrated that Test Time Augmentation (TTA) significantly increased the mAP@0.5 to 77.7%, surpassing the YOLOv8 model, which achieved 77.5% under the same conditions.

Solar power is a clean and sustainable energy source that does not emit greenhouse gases or other atmospheric pollutants. The inherent variability in solar energy due to random fluctuations introduces novel attributes to the power generation and load dynamics of the grid. Consequently, there has been growing attention to developing an accurate forecast model using various machine and deep learning techniques. Temporal attention mechanisms enable the model to concentrate on the critical components of the input sequence at each time step, thereby enhancing the accuracy of the prediction. The suggested GRU–temporal fusion transformer (GRU-TFT) model was trained and validated employing the “Daily Power Production of Solar Panels” Kaggle dataset. Furthermore, an innovative loss function termed DILATE is introduced to train the proposed model specifically for multistep and nonstationary time series forecasting. The outcomes have been subjected to a comparative analysis with alternative algorithms, such as neural basis expansion analysis for interpretable time series (N-BEATS), neural hierarchical interpolation for time series (N-HiTS), and extreme gradient boosting (XGBoost), using several evaluation metrics, including the absolute percentage error (MAE), mean square error (MSE), and root mean square error (RMSE). The model presented in this study exhibited significant performance improvements compared with traditional statistical and machine learning techniques. This is evident from the achieved values of MAE, MSE, and RMSE, which were 1.19, 2.08, and 1.44, respectively. In contrast, the machine learning approach utilizing the Holt–Winters method for time series forecasting in additive mode yielded MAE, MSE, and RMSE scores of 4.126, 29.105, and 5.3949, respectively.

Proper surveillance and maintenance of photovoltaic (PV) systems are crucial to ensure continuous power generation and prevent operational downtimes. However, manual analysis of electroluminescence (EL) images is subjective, time-intensive, and requires significant expertise. To address this issue, a comprehensive deep learning architecture has been developed for the semantic segmentation of 29 different features and defects within EL images of PV panels. The SegNet architecture encoder has been replaced with the VGG16 encoder, which incorporates pre-trained weights to leverage transfer learning during the feature extraction stage. A Convolutional Block Attention Module (CBAM) block has also been introduced to enhance the decoder’s ability to generate fine-grained segmentations. Additionally, the suggested architecture has been evaluated through the application of three different loss functions: weighted categorical cross-entropy loss, categorical cross-entropy, and focal loss. The Attention-Based SegNet architecture proposed with a weighted categorical cross-entropy loss exhibits superior performance in terms of accuracy, F1 score, intersection over union (IoU), precision, recall, mean IoU (mIoU), specificity, Jaccard index, and Dice coefficient. It achieves a Dice coefficient of 0.9408 and an mIoU of 0.9101, outperforming the state-of-the-art SEiPV-Net trained on the same dataset by 8.77% and 4.97%, respectively.

Recognizing and documenting Arabic sign language has recently received much attention because it enhances communication between deaf persons and normal people. The development of automatic sign language recognition (SLR) systems to allow communication with deaf persons is the primary goal of SLR. Until recently, Arabic SLR (ArSLR) received little attention. Building an automatic Arabic hand gesture recognition system is a challenging task. This work presents a novel image-based ArSL recognition approach where You Only Look Once v7 (YOLOv7) is used to build an accurate ArSL alphabet detector and classifier utilizing ArSL21L: Arabic Sign Language Letter Dataset. The proposed YOLOv7 medium model has achieved the highest mAP0.5 and mAP0.5:0.95 scores of 0.9909 and 0.8306, respectively. It has outperformed not only YOLOv5m but also YOLOv5l in terms of mAP0.5 and mAP0.5:0.95 scores. Furthermore, regarding mAP0.5 and mAP0.5:0.95 scores, the YOLOv7-tiny model has not only surpassed YOLOv5s but additionally YOLOv5m. YOLOv5s, on the other hand, has the lowest mAP0.5 and mAP0.5:0.95 scores of 0.9408 and 0.7661, respectively

The detection and classification of insect species represent challenging computer vision tasks that have significant applications in zoology and agriculture. Fortunately, biologists and taxonomists have developed a systematic approach to organizing organisms, which results in a hierarchical classification system. Insect classification employs a hierarchical structure that includes object detection at the order level, family classification, and species classification. However, the conventional insect identification method is time-consuming and requires the expertise of highly skilled taxonomists to identify insects accurately based on morphological characteristics. This paper presents a pioneering study on the automatic detection and classification of Arthropod Taxonomy Orders using an enhanced variant of the You Only Look Once (YOLOX) framework along with the Arthropod Taxonomy Orders Object Detection (ArTaxOr) Dataset. The proposed ArTaxOr dataset encompasses diverse arthropod species such as insects, spiders, crustaceans, centipedes, millipedes, and isopods. Moreover, some images within this dataset depict multiple species with varying sizes, shapes, and colors. Accordingly, all images are resized to 640 $$\times$$× 640 to ensure compliance with the requisite input image size for YOLOX. Further, mosaic augmentation is employed to enhance the model’s accuracy in recognizing small objects. Inspite of natural complexity in a majority of dataset images, the proposed YOLOX-based model attained superior mean average precision. The outcomes of this study could act as a standard against which forthcoming research in this domain could be compared or judged.