Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review

TL;DR

This review paper discusses recent advances, challenges, and future directions in vision-based industrial anomaly detection, emphasizing automation, data processing, and evaluation methods to improve manufacturing and aerospace inspection processes.

Contribution

It provides a comprehensive overview of vision-based anomaly detection studies since 2019, highlighting overlooked components, challenges, datasets, and future research directions.

Findings

Recent vision-based methods enable automatic feature extraction and interpretation.

Key challenges include data quality, learning mechanisms, and evaluation metrics.

Future directions focus on improving robustness, scalability, and real-time detection.

Abstract

Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.