Applications of Deep Learning Approaches for Defect Identification Trends, Challenges, and Future Directions

Abstract

Quality assurance in many fields relies on the ability to detect defects. This is especially true in the manufacturing, construction, electronics, and medical diagnostics sectors. As deep learning becomes more prevalent in industrial inspection systems, its revolutionary effect on defect identification is being felt in many different fields. This article summaries current methods for detecting defects in manufacturing, infrastructure, and biomedical imaging using deep learning. It examines methods such as CNNs, generative models, attention mechanisms, and emerging transformer and diffusion frameworks, focusing on their effectiveness in surface anomaly detection. The paper categorizes approaches into supervised, unsupervised, and semi-supervised models, examining their suitability under different data conditions and deployment scenarios. It also highlights key implementation challenges, including data imbalance, annotation complexity, dataset variability, and generalization across domains. There are also important items like the quality of the dataset, model interpretability, scalability, and real-time performance that are mentioned to ensure the successful implementation of AI in real-world scenarios. Future directions include the emergent technologies of domain-adaptive learning, explainable AI and the deployment of AI at the edge, where it could be applied to real-time inspection. The review summarizes the recent advances and highlights the methodologies to enhance the transparency and reliability of deep learning-based defect detection and the emergence of intelligent and high-performance adaptive inspection devices in industries.