Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production.

One of the primary challenges in ensuring road safety is effectively alerting drivers to adverse weather conditions, such as fog, which can severely impair visibility and increase the risk of accidents. Timely and accurate fog detection is crucial for providing drivers with the necessary warnings to adapt their driving behaviour and enhance safety. This paper presents a system designed to detect foggy scenarios and classify visibility levels, thereby enabling timely alerts for drivers to minimise the risks associated with reduced visibility. To achieve this, we have developed two new image datasets of road fog scenarios – Foggy‐Ceit 2023 and an extension to the Foggy CityScapes – DBF dataset – featuring both real and synthetic fog. Additionally, we compare various algorithms developed using classical vision techniques and deep learning methods (vision transformers [ViT] and EfficientNet). Finally, eXplainable artificial intelligence techniques are utilised to provide visual explanations and evaluate the performance of these models.