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.

As industrial camera become smarter, due to the increase in supporting algorithms, so the quality of data produced offers higher precision and accuracy of industrial inspection operations. This paper aims to focus on the accurate effect of camera focus on a calibration process under the influence of different sensor exposure and degrees of focus. A computer vision methodology is used to determine the effects of different sensor exposures and at different degrees of camera focus. Endocentric and telecentric lenses are used to acquire images, and a comparative analysis was achieved using the least square method. A sample of 2176 images was used to generate the population for analysis. The analysis showed that the human eye cannot visually determine a hundred percent focus of an image. This means that it is difficult to determine when an image is 100% focused from when it is 95% focused. The result from the experiment showed that as the camera focus tends towards 100% the calibration error decreases, with a minimum calibration error of 0.06 pixels, leading to the conclusion that the developed method achieved a calibration error accuracy with the most effective camera sensor exposure and the camera focus.

Worker safety is notably improved through the application of personal protective equipment (PPE), which effectively reduces the severity of injuries or fatal incidents in environments like construction sites, chemical facilities and hazardous areas. PPE is extensively mandated to ensure an acceptable level of safety, addressing not just accidents at the mentioned sites but also the risks posed by chemical hazards. Due to various factors or oversights, workers may intermittently fail to adhere to safety regulations regarding wearing protective equipment. Traditional manual monitoring is both labor-intensive and prone to errors. Thus, there is a pressing need for the advancement of intelligent monitoring systems capable of providing automated and precise detection of such safety equipment. As a solution, we present a deep learning approach for the real-time detection of PPE components, including helmets, safety boots, vests and gloves. The proposed deep learning model exhibited a remarkable mean average precision of 97.1%, indicating the model’s proficiency in object localization and recognition. These results not only underline the effectiveness of the deep learning -based PPE detection system but also emphasize its practicality in diverse industrial and occupational settings. By surpassing established benchmarks attained in literature, this research contributes significantly to enhancing safety standards and reducing the risk of workplace accidents.