Computer Vision-Based Anomaly Detection in Industrial Components

Anomaly detection in industrial components is essential for ensuring product quality and operational efficiency by identifying defects early. Advances in computer vision and deep learning have transformed traditional inspection, enabling automated systems to detect subtle anomalies with high accuracy. This survey reviews recent approaches, including supervised and unsupervised methods, focusing on deep neural networks, memory-augmented models, and feature clustering techniques that reduce reliance on labeled anomaly data. Advanced preprocessing, feature extraction, and patch-wise analysis enhance detection sensitivity and localization in high-resolution images. Real-time frameworks and lightweight architectures allow millisecond-level inference, suitable for production lines. The fusion of multiple data modalities, like RGB and depth data, further improves robustness in complex environments. However, challenges remain in handling data imbalance, distribution shifts, and generalizing models across diverse industrial settings. This survey discusses benchmark datasets, evaluation metrics, and future research directions, such as explainable AI, hybrid learning, and scalable adaptive systems for next-generation industrial anomaly detection.