Real-time non-intrusive monitoring of wind turbines, blades, and defect surfaces poses a set of complex challenges related to accuracy, safety, cost, and computational efficiency. This work introduces an enhanced deep learning-based framework for real-time detection of wind turbine blade defects. The WindDefNet is introduced, which features the Inception-ResNet modules, Visual Transformer (ViT), and multi-scale attention mechanisms. WindDefNet utilizes modified cross-convolutional blocks, including the powerful Inception-ResNet hybrid, to capture both fine-grained and high-level features from input images. A multi-scale attention module is added to focus on important regions in the image, improving detection accuracy, especially in challenging areas of the wind turbine blades. We employ pertaining to Inception-ResNet and ViT patch embedding architectures to achieve superior performance in defect classification. WindDefNet’s capability to capture and integrate multi-scale feature representations enhances its effectiveness for robust wind turbine condition monitoring, thereby reducing operational downtime and minimizing maintenance costs. Our model WindDefNet integrates a novel advanced attention mechanism, with custom-pretrained Inception-ResNet combining self-attention with a Visual Transformer encoder, to enhance feature extraction and improve model accuracy. The proposed method demonstrates significant improvements in classification performance, as evidenced by the evaluation metrics attain precision, recall, and F1-scores of 0.88, 1.00, and 0.93 for the damage, 1.00, 0.71, and 0.83 for the edge, and 1.00, 1.00, and 1.00 for both the erosion and normal surfaces. The macro-average and weighted-average F1 scores stand at 0.94, highlighting the robustness of our approach. These results underscore the potential of the proposed model for defect detection in industrial applications.

Fire detection is crucial for safeguarding lives and property. In recent years, advancements in deep learning and Internet of Things (IoT) architectures have significantly enhanced the accuracy of fire and smoke detection systems. In this study, we introduce FlareNet, a feature fusion based deep learning model that leverages a DenseNet architecture combined with Spatial Pyramid Pooling (SPP) and a Contextual Feature Pyramid Network (CFPN). FlareNet is further augmented with dual attention mechanisms and a Feature Enhancement Attention (FEA) mechanism to selectively emphasize critical features for distinguishing between fire and non-fire scenes. Our proposed model is rigorously evaluated across five diverse datasets: Sharma, Deep Quest, BoWFire, FD dataset, and our novel MixFire dataset, achieving an impressive average accuracy of 99.2%. A comparative evaluation against state-of-the-art (SOTA) algorithms reveals that FlareNet outperforms existing methods with a notable improvement in accuracy, precision, recall, and F1-score, thereby setting a new benchmark in the domain of fire detection. Furthermore, A comprehensive analysis with baseline models such as VGG16, VGG19, ResNet18, MobileNetV2, and DenseNet is also presented. These advancements underscore FlareNet’s capability to enhance fire detection systems for a more sustainable environment. FlareNet code and MixFire dataset can be accessed by https://github.com/adeelferozmirza/FlareNet.