The reliability of non-destructive testing methods at key stages of production plays a crucial role in ensuring the structural safety of the product and minimising production costs. Early detection of defects allows them to be rectified at minimal cost, helping to strengthen the business and increase economic sustainability. It also makes it possible to extend the life of components and reuse them. During operation, minor defects are usually caused by external factors such as human factors or environmental conditions. To improve the quality of product inspection, a new method is proposed involving a Decision Support System in the context of Second Opinion using Artificial Neural Networks. A functional diagram of the DSS architecture is developed, describing and justifying the choice of technologies to be implemented. First experiments were carried out to train the ANN YOLOv10 on a small consistent dataset and to test it on real images. The model demonstrates a sufficient ability to accurately predict the bounding box and class of low contrast small defects in over 50% of cases. Conversely, the accuracy of prediction for those categories with strong visual attributes rises to over 90%. This observation not only underscores the accuracy of the model, but also highlights the well-known problem of unbalanced data and local feature extraction. Such improvements promise to significantly increase the overall reliability of the results obtained. Further research will focus on collecting a specialised defect detection dataset and improving the model architecture.